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1. Refining the drift barrier hypothesis: a role of recessive gene count and an inhomogeneous Muller`s ratchet

Author

La Rocca, Luis A., Gerischer, Konrad, Bovier, Anton, and Krawitz, Peter M.

Subjects
Quantitative Biology - Populations and Evolution
Abstract

The drift-barrier hypothesis states that random genetic drift constrains the refinement of a phenotype under natural selection. The influence of effective population size and the genome-wide deleterious mutation rate were studied theoretically, and an inverse relationship between mutation rate and genome size has been observed for many species. However, the effect of the recessive gene count, an important feature of the genomic architecture, is unknown. In a Wright-Fisher model, we studied the mutation burden for a growing number of N completely recessive and lethal disease genes. Diploid individuals are represented with a binary $2 \times N$ matrix denoting wild-type and mutated alleles. Analytic results for specific cases were complemented by simulations across a broad parameter regime for gene count, mutation and recombination rates. Simulations revealed transitions to higher mutation burden and prevalence within a few generations that were linked to the extinction of the wild-type haplotype (least-loaded class). This metastability, that is, phases of quasi-equilibrium with intermittent transitions, persists over $100\,000$ generations. The drift-barrier hypothesis is confirmed by a high mutation burden resulting in population collapse. Simulations showed the emergence of mutually exclusive haplotypes for a mutation rate above 0.02 lethal equivalents per generation for a genomic architecture and population size representing complex multicellular organisms such as humans. In such systems, recombination proves pivotal, preventing population collapse and maintaining a mutation burden below 10. This study advances our understanding of gene pool stability, and particularly the role of the number of recessive disorders. Insights into Muller`s ratchet dynamics are provided, and the essential role of recombination in curbing mutation burden and stabilizing the gene pool is demonstrated., Comment: 21 pages, 4 figures

Published
2024

3. Next-generation phenotyping integrated in a national framework for patients with ultrarare disorders improves genetic diagnostics and yields new molecular findings

Author

Schmidt, Axel, Danyel, Magdalena, Grundmann, Kathrin, Brunet, Theresa, Klinkhammer, Hannah, Hsieh, Tzung-Chien, Engels, Hartmut, Peters, Sophia, Knaus, Alexej, Moosa, Shahida, Averdunk, Luisa, Boschann, Felix, Sczakiel, Henrike Lisa, Schwartzmann, Sarina, Mensah, Martin Atta, Pantel, Jean Tori, Holtgrewe, Manuel, Bösch, Annemarie, Weiß, Claudia, Weinhold, Natalie, Suter, Aude-Annick, Stoltenburg, Corinna, Neugebauer, Julia, Kallinich, Tillmann, Kaindl, Angela M., Holzhauer, Susanne, Bührer, Christoph, Bufler, Philip, Kornak, Uwe, Ott, Claus-Eric, Schülke, Markus, Nguyen, Hoa Huu Phuc, Hoffjan, Sabine, Grasemann, Corinna, Rothoeft, Tobias, Brinkmann, Folke, Matar, Nora, Sivalingam, Sugirthan, Perne, Claudia, Mangold, Elisabeth, Kreiss, Martina, Cremer, Kirsten, Betz, Regina C., Mücke, Martin, Grigull, Lorenz, Klockgether, Thomas, Spier, Isabel, Heimbach, André, Bender, Tim, Brand, Fabian, Stieber, Christiane, Morawiec, Alexandra Marzena, Karakostas, Pantelis, Schäfer, Valentin S., Bernsen, Sarah, Weydt, Patrick, Castro-Gomez, Sergio, Aziz, Ahmad, Grobe-Einsler, Marcus, Kimmich, Okka, Kobeleva, Xenia, Önder, Demet, Lesmann, Hellen, Kumar, Sheetal, Tacik, Pawel, Basin, Meghna Ahuja, Incardona, Pietro, Lee-Kirsch, Min Ae, Berner, Reinhard, Schuetz, Catharina, Körholz, Julia, Kretschmer, Tanita, Di Donato, Nataliya, Schröck, Evelin, Heinen, André, Reuner, Ulrike, Hanßke, Amalia-Mihaela, Kaiser, Frank J., Manka, Eva, Munteanu, Martin, Kuechler, Alma, Cordula, Kiewert, Hirtz, Raphael, Schlapakow, Elena, Schlein, Christian, Lisfeld, Jasmin, Kubisch, Christian, Herget, Theresia, Hempel, Maja, Weiler-Normann, Christina, Ullrich, Kurt, Schramm, Christoph, Rudolph, Cornelia, Rillig, Franziska, Groffmann, Maximilian, Muntau, Ania, Tibelius, Alexandra, Schwaibold, Eva M. C., Schaaf, Christian P., Zawada, Michal, Kaufmann, Lilian, Hinderhofer, Katrin, Okun, Pamela M., Kotzaeridou, Urania, Hoffmann, Georg F., Choukair, Daniela, Bettendorf, Markus, Spielmann, Malte, Ripke, Annekatrin, Pauly, Martje, Münchau, Alexander, Lohmann, Katja, Hüning, Irina, Hanker, Britta, Bäumer, Tobias, Herzog, Rebecca, Hellenbroich, Yorck, Westphal, Dominik S., Strom, Tim, Kovacs, Reka, Riedhammer, Korbinian M., Mayerhanser, Katharina, Graf, Elisabeth, Brugger, Melanie, Hoefele, Julia, Oexle, Konrad, Mirza-Schreiber, Nazanin, Berutti, Riccardo, Schatz, Ulrich, Krenn, Martin, Makowski, Christine, Weigand, Heike, Schröder, Sebastian, Rohlfs, Meino, Vill, Katharina, Hauck, Fabian, Borggraefe, Ingo, Müller-Felber, Wolfgang, Kurth, Ingo, Elbracht, Miriam, Knopp, Cordula, Begemann, Matthias, Kraft, Florian, Lemke, Johannes R., Hentschel, Julia, Platzer, Konrad, Strehlow, Vincent, Abou Jamra, Rami, Kehrer, Martin, Demidov, German, Beck-Wödl, Stefanie, Graessner, Holm, Sturm, Marc, Zeltner, Lena, Schöls, Ludger J., Magg, Janine, Bevot, Andrea, Kehrer, Christiane, Kaiser, Nadja, Turro, Ernest, Horn, Denise, Grüters-Kieslich, Annette, Klein, Christoph, Mundlos, Stefan, Nöthen, Markus, Riess, Olaf, Meitinger, Thomas, Krude, Heiko, Krawitz, Peter M., Haack, Tobias, Ehmke, Nadja, and Wagner, Matias

Published
2024
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4. GestaltMML: Enhancing Rare Genetic Disease Diagnosis through Multimodal Machine Learning Combining Facial Images and Clinical Texts

Author

Wu, Da, Yang, Jingye, Liu, Cong, Hsieh, Tzung-Chien, Marchi, Elaine, Blair, Justin, Krawitz, Peter, Weng, Chunhua, Chung, Wendy, Lyon, Gholson J., Krantz, Ian D., Kalish, Jennifer M., and Wang, Kai

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Multimedia, Quantitative Biology - Genomics
Abstract

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of time. Addressing this "diagnostic odyssey" thus has substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. Existing methods using frontal facial photos were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates facial images, demographic information (age, sex, ethnicity), and clinical notes (optionally, a list of Human Phenotype Ontology terms) to improve prediction accuracy. Furthermore, we also evaluated GestaltMML on a diverse range of datasets, including 528 diseases from the GestaltMatcher Database, several in-house datasets of Beckwith-Wiedemann syndrome (BWS, over-growth syndrome with distinct facial features), Sotos syndrome (overgrowth syndrome with overlapping features with BWS), NAA10-related neurodevelopmental syndrome, Cornelia de Lange syndrome (multiple malformation syndrome), and KBG syndrome (multiple malformation syndrome). Our results suggest that GestaltMML effectively incorporates multiple modalities of data, greatly narrowing candidate genetic diagnoses of rare diseases and may facilitate the reinterpretation of genome/exome sequencing data., Comment: Significant revisions

Published
2023

5. GANonymization: A GAN-based Face Anonymization Framework for Preserving Emotional Expressions

Author

Hellmann, Fabio, Mertes, Silvan, Benouis, Mohamed, Hustinx, Alexander, Hsieh, Tzung-Chien, Conati, Cristina, Krawitz, Peter, and André, Elisabeth

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we introduce GANonymization, a novel face anonymization framework with facial expression-preserving abilities. Our approach is based on a high-level representation of a face, which is synthesized into an anonymized version based on a generative adversarial network (GAN). The effectiveness of the approach was assessed by evaluating its performance in removing identifiable facial attributes to increase the anonymity of the given individual face. Additionally, the performance of preserving facial expressions was evaluated on several affect recognition datasets and outperformed the state-of-the-art methods in most categories. Finally, our approach was analyzed for its ability to remove various facial traits, such as jewelry, hair color, and multiple others. Here, it demonstrated reliable performance in removing these attributes. Our results suggest that GANonymization is a promising approach for anonymizing faces while preserving facial expressions., Comment: 26 pages, 11 figures, 6 tables, ACM Transactions on Multimedia Computing, Communications, and Applications

Published
2023
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6. Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles

Author

Hustinx, Alexander, Hellmann, Fabio, Sümer, Ömer, Javanmardi, Behnam, André, Elisabeth, Krawitz, Peter, and Hsieh, Tzung-Chien

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Quantitative Biology - Genomics
Abstract

Rare genetic disorders affect more than 6% of the global population. Reaching a diagnosis is challenging because rare disorders are very diverse. Many disorders have recognizable facial features that are hints for clinicians to diagnose patients. Previous work, such as GestaltMatcher, utilized representation vectors produced by a DCNN similar to AlexNet to match patients in high-dimensional feature space to support "unseen" ultra-rare disorders. However, the architecture and dataset used for transfer learning in GestaltMatcher have become outdated. Moreover, a way to train the model for generating better representation vectors for unseen ultra-rare disorders has not yet been studied. Because of the overall scarcity of patients with ultra-rare disorders, it is infeasible to directly train a model on them. Therefore, we first analyzed the influence of replacing GestaltMatcher DCNN with a state-of-the-art face recognition approach, iResNet with ArcFace. Additionally, we experimented with different face recognition datasets for transfer learning. Furthermore, we proposed test-time augmentation, and model ensembles that mix general face verification models and models specific for verifying disorders to improve the disorder verification accuracy of unseen ultra-rare disorders. Our proposed ensemble model achieves state-of-the-art performance on both seen and unseen disorders.

Published
2022
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7. Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders

Author

Sümer, Ömer, Hellmann, Fabio, Hustinx, Alexander, Hsieh, Tzung-Chien, André, Elisabeth, and Krawitz, Peter

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Multimedia
Abstract

Computer vision-based methods have valuable use cases in precision medicine, and recognizing facial phenotypes of genetic disorders is one of them. Many genetic disorders are known to affect faces' visual appearance and geometry. Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem and used deep learning methods. The challenging issue in practice is the sparse label distribution and huge class imbalances across categories. Furthermore, most disorders have few labeled samples in training sets, making representation learning and generalization essential to acquiring a reliable feature descriptor. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition. Furthermore, we created simple baselines of few-shot meta-learning methods to improve our base feature descriptor. Our quantitative results on GestaltMatcher Database show that our CNN baseline surpasses previous works, including GestaltMatcher, and few-shot meta-learning strategies improve retrieval performance in frequent and rare classes., Comment: This paper is accepted for publication at MIE 2023 Conference

Published
2022
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8. Delineation of the adult phenotype of Coffin–Siris syndrome in 35 individuals

Author

Schmetz, Ariane, Lüdecke, Hermann-Josef, Surowy, Harald, Sivalingam, Sugirtahn, Bruel, Ange-Line, Caumes, Roseline, Charles, Perrine, Chatron, Nicolas, Chrzanowska, Krystyna, Codina-Solà, Marta, Colson, Cindy, Cuscó, Ivon, Denommé-Pichon, Anne-Sophie, Edery, Patrick, Faivre, Laurence, Green, Andrew, Heide, Solveig, Hsieh, Tzung-Chien, Hustinx, Alexander, Kleinendorst, Lotte, Knopp, Cordula, Kraft, Florian, Krawitz, Peter M., Lasa-Aranzasti, Amaia, Lesca, Gaetan, López-González, Vanesa, Maraval, Julien, Mignot, Cyril, Neuhann, Teresa, Netzer, Christian, Oehl-Jaschkowitz, Barbara, Petit, Florence, Philippe, Christophe, Posmyk, Renata, Putoux, Audrey, Reis, André, Sánchez-Soler, María José, Suh, Julia, Tkemaladze, Tinatin, Tran Mau Them, Frédéric, Travessa, André, Trujillano, Laura, Valenzuela, Irene, van Haelst, Mieke M., Vasileiou, Georgia, Vincent-Delorme, Catherine, Walther, Mona, Verde, Pablo, Bramswig, Nuria C., and Wieczorek, Dagmar

Published
2024
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11. CUX1-related neurodevelopmental disorder: deep insights into phenotype-genotype spectrum and underlying pathology

Author

Oppermann, Henry, Marcos-Grañeda, Elia, Weiss, Linnea A., Gurnett, Christina A., Jelsig, Anne Marie, Vineke, Susanne H., Isidor, Bertrand, Mercier, Sandra, Magnussen, Kari, Zacher, Pia, Hashim, Mona, Pagnamenta, Alistair T., Race, Simone, Srivastava, Siddharth, Frazier, Zoë, Maiwald, Robert, Pergande, Matthias, Milani, Donatella, Rinelli, Martina, Levy, Jonathan, Krey, Ilona, Fontana, Paolo, Lonardo, Fortunato, Riley, Stephanie, Kretzer, Jasmine, Rankin, Julia, Reis, Linda M., Semina, Elena V., Reuter, Miriam S., Scherer, Stephen W., Iascone, Maria, Weis, Denisa, Fagerberg, Christina R., Brasch-Andersen, Charlotte, Hansen, Lars Kjaersgaard, Kuechler, Alma, Noble, Nathan, Gardham, Alice, Tenney, Jessica, Rathore, Geetanjali, Beck-Woedl, Stefanie, Haack, Tobias B., Pavlidou, Despoina C., Atallah, Isis, Vodopiutz, Julia, Janecke, Andreas R., Hsieh, Tzung-Chien, Lesmann, Hellen, Klinkhammer, Hannah, Krawitz, Peter M., Lemke, Johannes R., Jamra, Rami Abou, Nieto, Marta, Tümer, Zeynep, and Platzer, Konrad

Published
2023
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12. The GA4GH Phenopacket schema defines a computable representation of clinical data

Author

Jacobsen, Julius OB, Baudis, Michael, Baynam, Gareth S, Beckmann, Jacques S, Beltran, Sergi, Buske, Orion J, Callahan, Tiffany J, Chute, Christopher G, Courtot, Mélanie, Danis, Daniel, Elemento, Olivier, Essenwanger, Andrea, Freimuth, Robert R, Gargano, Michael A, Groza, Tudor, Hamosh, Ada, Harris, Nomi L, Kaliyaperumal, Rajaram, Lloyd, Kevin C Kent, Khalifa, Aly, Krawitz, Peter M, Köhler, Sebastian, Laraway, Brian J, Lehväslaiho, Heikki, Matalonga, Leslie, McMurry, Julie A, Metke-Jimenez, Alejandro, Mungall, Christopher J, Munoz-Torres, Monica C, Ogishima, Soichi, Papakonstantinou, Anastasios, Piscia, Davide, Pontikos, Nikolas, Queralt-Rosinach, Núria, Roos, Marco, Sass, Julian, Schofield, Paul N, Seelow, Dominik, Siapos, Anastasios, Smedley, Damian, Smith, Lindsay D, Steinhaus, Robin, Sundaramurthi, Jagadish Chandrabose, Swietlik, Emilia M, Thun, Sylvia, Vasilevsky, Nicole A, Wagner, Alex H, Warner, Jeremy L, Weiland, Claus, Haendel, Melissa A, and Robinson, Peter N

Subjects
GAGH Phenopacket Modeling Consortium
Published
2022

14. Episignature analysis of moderate effects and mosaics

Author

Oexle, Konrad, Zech, Michael, Stühn, Lara G., Siegert, Sandy, Brunet, Theresa, Schmidt, Wolfgang M., Wagner, Matias, Schmidt, Axel, Engels, Hartmut, Tilch, Erik, Monestier, Olivier, Destrėe, Anne, Hanker, Britta, Boesch, Sylvia, Jech, Robert, Berutti, Riccardo, Kaiser, Frank, Haslinger, Bernhard, Haack, Tobias B., Garavaglia, Barbara, Krawitz, Peter, Winkelmann, Juliane, and Mirza-Schreiber, Nazanin

Published
2023
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15. Expanding the phenotypic spectrum of NAA10-related neurodevelopmental syndrome and NAA15-related neurodevelopmental syndrome

Author

Lyon, Gholson J., Vedaie, Marall, Beisheim, Travis, Park, Agnes, Marchi, Elaine, Gottlieb, Leah, Hsieh, Tzung-Chien, Klinkhammer, Hannah, Sandomirsky, Katherine, Cheng, Hanyin, Starr, Lois J., Preddy, Isabelle, Tseng, Marcellus, Li, Quan, Hu, Yu, Wang, Kai, Carvalho, Ana, Martinez, Francisco, Caro-Llopis, Alfonso, Gavin, Maureen, Amble, Karen, Krawitz, Peter, Marmorstein, Ronen, and Herr-Israel, Ellen

Published
2023
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16. Role of CAMK2D in neurodevelopment and associated conditions

Author

Rigter, Pomme M.F., de Konink, Charlotte, Dunn, Matthew J., Proietti Onori, Martina, Humberson, Jennifer B., Thomas, Matthew, Barnes, Caitlin, Prada, Carlos E., Weaver, K. Nicole, Ryan, Thomas D., Caluseriu, Oana, Conway, Jennifer, Calamaro, Emily, Fong, Chin-To, Wuyts, Wim, Meuwissen, Marije, Hordijk, Eva, Jonkers, Carsten N., Anderson, Lucas, Yuseinova, Berfin, Polonia, Sarah, Beysen, Diane, Stark, Zornitza, Savva, Elena, Poulton, Cathryn, McKenzie, Fiona, Bhoj, Elizabeth, Bupp, Caleb P., Bézieau, Stéphane, Mercier, Sandra, Blevins, Amy, Wentzensen, Ingrid M., Xia, Fan, Rosenfeld, Jill A., Hsieh, Tzung-Chien, Krawitz, Peter M., Elbracht, Miriam, Veenma, Danielle C.M., Schulman, Howard, Stratton, Margaret M., Küry, Sébastien, and van Woerden, Geeske M.

Published
2024
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17. The neurodevelopmental and facial phenotype in individuals with a TRIP12 variant

Author

Aerden, Mio, Denommé-Pichon, Anne-Sophie, Bonneau, Dominique, Bruel, Ange-Line, Delanne, Julian, Gérard, Bénédicte, Mazel, Benoît, Philippe, Christophe, Pinson, Lucile, Prouteau, Clément, Putoux, Audrey, Tran Mau-Them, Frédéric, Viora-Dupont, Éléonore, Vitobello, Antonio, Ziegler, Alban, Piton, Amélie, Isidor, Bertrand, Francannet, Christine, Maillard, Pierre-Yves, Julia, Sophie, Philippe, Anais, Schaefer, Elise, Koene, Saskia, Ruivenkamp, Claudia, Hoffer, Mariette, Legius, Eric, Theunis, Miel, Keren, Boris, Buratti, Julien, Charles, Perrine, Courtin, Thomas, Misra-Isrie, Mala, van Haelst, Mieke, Waisfisz, Quinten, Wieczorek, Dagmar, Schmetz, Ariane, Herget, Theresia, Kortüm, Fanny, Lisfeld, Jasmin, Debray, François-Guillaume, Bramswig, Nuria C., Atallah, Isis, Fodstad, Heidi, Jouret, Guillaume, Almoguera, Berta, Tahsin-Swafiri, Saoud, Santos-Simarro, Fernando, Palomares-Bralo, Maria, López-González, Vanesa, Kibaek, Maria, Tørring, Pernille M., Renieri, Alessandra, Bruno, Lucia Pia, Õunap, Katrin, Wojcik, Monica, Hsieh, Tzung-Chien, Krawitz, Peter, and Van Esch, Hilde

Published
2023
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18. A lower prevalence for recessive disorders in a random mating population is a transient phenomenon during and after a growth phase

Author

La Rocca, Luis A., Frank, Julia, Bentzen, Heidi Beate, Pantel, Jean-Tori, Gerischer, Konrad, Bovier, Anton, and Krawitz, Peter M.

Subjects
Quantitative Biology - Populations and Evolution
Abstract

Despite increasing data from population-wide sequencing studies, the risk for recessive disorders in consanguineous partnerships is still heavily debated. An important aspect that has not sufficiently been investigated theoretically, is the influence of inbreeding on mutation load and incidence rates when the population sizes change. We therefore developed a model to study these dynamics for a wide range of growth and mating conditions. In the phase of population expansion and shortly afterwards, our simulations show that there is a drop of diseased individuals at the expense of an increasing mutation load for random mating, while both parameters remain almost constant in highly consanguineous partnerships. This explains the empirical observation in present times that a high degree of consanguinity is associated with an increased risk of autosomal recessive disorders. However, it also states that the higher frequency of severe recessive disorders with developmental delay in inbred populations is a transient phenomenon before a mutation-selection balance is reached again., Comment: A video clip of our simulations can be found at https://youtu.be/5hOgLyRqWPg

Published
2020

22. Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence

Author

Hassanin, Emadeldin, Spier, Isabel, Bobbili, Dheeraj R., Aldisi, Rana, Klinkhammer, Hannah, David, Friederike, Dueñas, Nuria, Hüneburg, Robert, Perne, Claudia, Brunet, Joan, Capella, Gabriel, Nöthen, Markus M., Forstner, Andreas J., Mayr, Andreas, Krawitz, Peter, May, Patrick, Aretz, Stefan, and Maj, Carlo

Published
2023
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24. Biallelic variants in CRIPT cause a Rothmund-Thomson-like syndrome with increased cellular senescence

Author

Averdunk, Luisa, Huetzen, Maxim A., Moreno-Andrés, Daniel, Kalb, Reinhard, McKee, Shane, Hsieh, Tzung-Chien, Seibt, Annette, Schouwink, Marten, Lalani, Seema, Faqeih, Eissa Ali, Brunet, Theresa, Boor, Peter, Neveling, Kornelia, Hoischen, Alexander, Hildebrandt, Barbara, Graf, Elisabeth, Lu, Linchao, Jin, Weidong, Schaper, Joerg, Omer, Jamal A., Demaret, Tanguy, Fleischer, Nicole, Schindler, Detlev, Krawitz, Peter, Mayatepek, Ertan, Wieczorek, Dagmar, Wang, Lisa L., Antonin, Wolfram, Jachimowicz, Ron D., von Felbert, Verena, and Distelmaier, Felix

Published
2023
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30. DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning

Author

Gurovich, Yaron, Hanani, Yair, Bar, Omri, Fleischer, Nicole, Gelbman, Dekel, Basel-Salmon, Lina, Krawitz, Peter, Kamphausen, Susanne B, Zenker, Martin, Bird, Lynne M., and Gripp, Karen W.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Facial analysis technologies have recently measured up to the capabilities of expert clinicians in syndrome identification. To date, these technologies could only identify phenotypes of a few diseases, limiting their role in clinical settings where hundreds of diagnoses must be considered. We developed a facial analysis framework, DeepGestalt, using computer vision and deep learning algorithms, that quantifies similarities to hundreds of genetic syndromes based on unconstrained 2D images. DeepGestalt is currently trained with over 26,000 patient cases from a rapidly growing phenotype-genotype database, consisting of tens of thousands of validated clinical cases, curated through a community-driven platform. DeepGestalt currently achieves 91% top-10-accuracy in identifying over 215 different genetic syndromes and has outperformed clinical experts in three separate experiments. We suggest that this form of artificial intelligence is ready to support medical genetics in clinical and laboratory practices and will play a key role in the future of precision medicine.

Published
2018

31. Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.

Author

Klinkhammer, Hannah, Staerk, Christian, Maj, Carlo, Krawitz, Peter M., and Mayr, Andreas

Abstract

Polygenic risk scores (PRS) aim to predict a trait from genetic information, relying on common genetic variants with low to medium effect sizes. As genotype data are high‐dimensional in nature, it is crucial to develop methods that can be applied to large‐scale data (large n$$ n $$ and large p$$ p $$). Many PRS tools aggregate univariate summary statistics from genome‐wide association studies into a single score. Recent advancements allow simultaneous modeling of variant effects from individual‐level genotype data. In this context, we introduced snpboost, an algorithm that applies statistical boosting on individual‐level genotype data to estimate PRS via multivariable regression models. By processing variants iteratively in batches, snpboost can deal with large‐scale cohort data. Having solved the technical obstacles due to data dimensionality, the methodological scope can now be broadened—focusing on key objectives for the clinical application of PRS. Similar to most methods in this context, snpboost has, so far, been restricted to quantitative and binary traits. Now, we incorporate more advanced alternatives—targeted to the particular aim and outcome. Adapting the loss function extends the snpboost framework to further data situations such as time‐to‐event and count data. Furthermore, alternative loss functions for continuous outcomes allow us to focus not only on the mean of the conditional distribution but also on other aspects that may be more helpful in the risk stratification of individual patients and can quantify prediction uncertainty, for example, median or quantile regression. This work enhances PRS fitting across multiple model classes previously unfeasible for this data type. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Acromesomelic Dysplasia With Homozygosity for a Likely Pathogenic BMPR1B Variant: Postaxial Polydactyly as a Novel Clinical Finding.

Author

Abdelrazek, Ibrahim M., Knaus, Alexej, Javanmardi, Behnam, Krawitz, Peter M., Horn, Denise, Abdalla, Ebtesam M., and Kumar, Sheetal

Subjects
BONE morphogenetic protein receptors, GROWTH differentiation factors, DYSPLASIA, CONSANGUINITY, SHORT stature, GENETIC variation
Abstract

Background: Acromesomelic chondrodysplasias are a rare subgroup of the clinically and genetically heterogeneous osteochondrodysplasias that are characterised by abnormalities in the limb development and short stature. Here, we report a 2‐year‐old boy, offspring of consanguineous parents, with acromesomelic dysplasia and postaxial polydactyly in which exome sequencing identified a novel homozygous missense variant in BMPR1B. The patient showed skeletal malformation of both hands and feet that included complex brachydactyly with the thumbs most severely affected, postaxial polydactyly of both hands, shortened toes as well as a bilateral hypoplasia of the fibula. Methods: Whole trio exome sequencing was conducted to identify potential genetic variants in the patient. Results: The analysis identified the biallelic variant NM_001203.3:c.821A > G;p.(Gln274Arg) in BMPR1B, a gene encoding bone morphogenetic protein receptor 1B. Conclusion: The skeletal phenotype can be brought in line with the phenotypes of previously reported cases of BMPR1B‐associated chondrodysplasias. However, the postaxial polydactyly described here is a novel clinical finding in a BMPR1B‐related case; notably, it has previously been reported in other acromesomelic dysplasia cases caused by homozygous pathogenic variants in GDF5—a gene which encodes for growth differentiation factor 5, a high‐affinity ligand to BMPR1B. [ABSTRACT FROM AUTHOR]

Published
2024
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34. GestaltMatcher facilitates rare disease matching using facial phenotype descriptors

Author

Hsieh, Tzung-Chien, Bar-Haim, Aviram, Moosa, Shahida, Ehmke, Nadja, Gripp, Karen W., Pantel, Jean Tori, Danyel, Magdalena, Mensah, Martin Atta, Horn, Denise, Rosnev, Stanislav, Fleischer, Nicole, Bonini, Guilherme, Hustinx, Alexander, Schmid, Alexander, Knaus, Alexej, Javanmardi, Behnam, Klinkhammer, Hannah, Lesmann, Hellen, Sivalingam, Sugirthan, Kamphans, Tom, Meiswinkel, Wolfgang, Ebstein, Frédéric, Krüger, Elke, Küry, Sébastien, Bézieau, Stéphane, Schmidt, Axel, Peters, Sophia, Engels, Hartmut, Mangold, Elisabeth, Kreiß, Martina, Cremer, Kirsten, Perne, Claudia, Betz, Regina C., Bender, Tim, Grundmann-Hauser, Kathrin, Haack, Tobias B., Wagner, Matias, Brunet, Theresa, Bentzen, Heidi Beate, Averdunk, Luisa, Coetzer, Kimberly Christine, Lyon, Gholson J., Spielmann, Malte, Schaaf, Christian P., Mundlos, Stefan, Nöthen, Markus M., and Krawitz, Peter M.

Published
2022
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36. Cross-tissue transcriptome-wide association studies identify susceptibility genes shared between schizophrenia and inflammatory bowel disease

Author

Uellendahl-Werth, Florian, Maj, Carlo, Borisov, Oleg, Juzenas, Simonas, Wacker, Eike Matthias, Jørgensen, Isabella Friis, Steiert, Tim Alexander, Bej, Saptarshi, Krawitz, Peter, Hoffmann, Per, Schramm, Christoph, Wolkenhauer, Olaf, Banasik, Karina, Brunak, Søren, Schreiber, Stefan, Karlsen, Tom Hemming, Degenhardt, Franziska, Nöthen, Markus, Franke, Andre, Folseraas, Trine, and Ellinghaus, David

Published
2022
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37. GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Author

Hsieh, Tzung-Chien, primary, Lesmann, Hellen, additional, Hustinx, Alexander, additional, Moosa, Shahida, additional, Marchi, Elaine, additional, Martin, Maria del Pilar Caro, additional, Abdelrazek, Ibrahim, additional, Pantel, Jean Tori, additional, Klinkhammer, Hannah, additional, Hagen, Merle ten, additional, Thong, Meow-Keong, additional, Mazlan, Rifhan Azwani Binti, additional, Tae, Sok Kun, additional, Kamphans, Tom, additional, Meiswinkel, Wolfgang, additional, Li, Jing-Mei, additional, Javanmardi, Behnam, additional, Knaus, Alexej, additional, Uwineza, Annette, additional, Knopp, Cordula, additional, Tkemaladze, Tinatin, additional, Elbracht, Miriam, additional, Mattern, Larissa, additional, Jamra, Rami Abou, additional, Velmans, Clara, additional, Strehlow, Vincent, additional, Jacob, Maureen, additional, Peron, Angela, additional, Dias, Cristina, additional, Nunes, Beatriz, additional, Vilella, Thainá, additional, Pinheiro, Isabel, additional, Kim, Chong, additional, Melaragno, Maria, additional, Weiland, Hannah, additional, Kaptain, Sophia, additional, Chwiałkowska, Karolina , additional, Kwasniewski, Miroslaw, additional, Saad, Ramy, additional, Wiethoff, Sarah, additional, Goel, Himanshu, additional, Tang, Clara, additional, Hau, Anna, additional, Barakat, Tahsin Stefan, additional, Panek, Przemysław, additional, Nabil, Amira, additional, Suh, Julia, additional, Braun, Frederik, additional, Gomy, Israel, additional, Averdunk, Luisa, additional, Ekure, Ekanem, additional, Bergant, Gaber, additional, Peterlin, Borut, additional, Graziano, Claudio, additional, Gaboon, Nagwa, additional, Fiesco-Roa, Moisés, additional, Spinelli, Alessandro, additional, Wilpert, Nina-Maria, additional, Phowthongkum, Prasit, additional, Güzel, Nergis, additional, Haack, Tobias, additional, Bitar, Rana, additional, Tzschach, Andreas, additional, Rodriguez-Palmero, Agusti, additional, Brunet, Theresa, additional, Rudnik-Schöneborn, Sabine, additional, Contreras-Capetillo, Silvina, additional, Oberlack, Ava, additional, Samango-Sprouse, Carole, additional, Sadeghin, Teresa, additional, Olaya, Margaret, additional, Platzer, Konrad, additional, Borovikov, Artem, additional, Schnabel, Franziska, additional, Heuft, Lara, additional, Herrmann, Vera, additional, Oegema, Renske, additional, Elkhateeb, Nour, additional, Kumar, Sheetal, additional, Komlosi, Katalin, additional, Mohamed, Khoushoua, additional, Kalantari, Silvia, additional, Sirchia, Fabio, additional, Martinez-Monseny, Antonio, additional, Höller, Matthias, additional, Mohamed, Amal, additional, Lasa-Aranzasti, Amaia, additional, Sayer, John, additional, Ehmke, Nadja, additional, Danyel, Magdalena, additional, Sczakiel, Henrike, additional, Schwartzmann, Sarina, additional, Boschann, Felix, additional, Zhao, Max, additional, Adams, Ronja, additional, Einicke, Lara, additional, Horn, Denise, additional, Chew, Kee Seang, additional, Kam, Choy Chen, additional, Karakoyun, Miray, additional, Pode-Shakked, Ben, additional, Eliyahu, Aviva, additional, Rock, Rachel, additional, Carrion, Teresa, additional, Chorin, Odelia, additional, Zarate, Yuri, additional, Martinez, Marcello, additional, Karakaya, Mert, additional, Tung, Moon Ley, additional, Chandra, Bharatendu, additional, Lumaka, Aimé, additional, Shinawi, Marwan, additional, Blackburn, Patrick, additional, Wang, Tianyun, additional, Niehues, Tim, additional, Hu, Ping, additional, Waikel, Rebekah, additional, Hanchard, Suzanna Ledgister, additional, Elmakkawy, Gehad, additional, Safwat, Sylvia, additional, Ebstein, Frédéric, additional, Krüger, Elke, additional, Küry, Sébastien, additional, Bezieau, Stephane, additional, Arlt, Annabelle, additional, Marbach, Felix, additional, Li, Dong, additional, Dupuis, Lucie, additional, Mendoza-Londono, Roberto, additional, Houge, Sofia Douzgou, additional, Weis, Denisa, additional, Chung, Brian, additional, Mak, Christopher, additional, Elcioglu, Nursel, additional, Aykut, Ayca, additional, Şimşek-Kiper, Peli, additional, Bögershausen, Nina, additional, Wollnik, Bernd, additional, Bentzen, Heidi Beate, additional, Kurth, Ingo, additional, Netzer, Christian, additional, Jezela-Stanek, Aleksandra, additional, Devriendt, Koen, additional, Gripp, Karen, additional, Mücke, Martin, additional, Verloes, Alain, additional, Schaaf, Christian, additional, Nellåker, Christoffer, additional, Solomon, Benjamin, additional, Nöthen, Markus, additional, Abdalla, Ebtesam, additional, Lyon, Gholson, additional, Krawitz, Peter, additional, Kayserili, Hulya, additional, Toutouna, Louiza, additional, Schmidt, Axel, additional, Roth, Regina, additional, Wieczorek, Dagmar, additional, and Olinger, Eric, additional

Published
2024
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39. Impact of Synbiotic Intake on Liver Metabolism in Metabolically Healthy Participants and Its Potential Preventive Effect on Metabolic-Dysfunction-Associated Fatty Liver Disease (MAFLD): A Randomized, Placebo-Controlled, Double-Blinded Clinical Trial

Author

Mantri, Aakash, primary, Köhlmoos, Anika, additional, Schelski, Daniela Stephanie, additional, Seel, Waldemar, additional, Stoffel-Wagner, Birgit, additional, Krawitz, Peter, additional, Stehle, Peter, additional, Holst, Jens Juul, additional, Weber, Bernd, additional, Koban, Leonie, additional, Plassmann, Hilke, additional, and Simon, Marie-Christine, additional

Published
2024
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41. Polygenic Modeling of Muscle Fibers Composition

Author

Borisov, Oleg, Maj, Carlo, Kulemin, Nikolay, Semenova, Ekaterina, Krawitz, Peter, Ahmetov, Ildus, Generozov, Edward, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Lames, Martin, editor, Danilov, Alexander, editor, Timme, Egor, editor, and Vassilevski, Yuri, editor

Published
2020
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45. Genome sequencing in families with congenital limb malformations

Author

Elsner, Jonas, Mensah, Martin A., Holtgrewe, Manuel, Hertzberg, Jakob, Bigoni, Stefania, Busche, Andreas, Coutelier, Marie, de Silva, Deepthi C., Elçioglu, Nursel, Filges, Isabel, Gerkes, Erica, Girisha, Katta M., Graul-Neumann, Luitgard, Jamsheer, Aleksander, Krawitz, Peter, Kurth, Ingo, Markus, Susanne, Megarbane, Andre, Reis, André, Reuter, Miriam S., Svoboda, Daniel, Teller, Christopher, Tuysuz, Beyhan, Türkmen, Seval, Wilson, Meredith, Woitschach, Rixa, Vater, Inga, Caliebe, Almuth, Hülsemann, Wiebke, Horn, Denise, Mundlos, Stefan, and Spielmann, Malte

Published
2021
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46. Spliceosome malfunction causes neurodevelopmental disorders with overlapping features

Author

Genetica, Genetica Sectie Research, Cancer, Child Health, Li, Dong, Wang, Qin, Bayat, Allan, Battig, Mark R., Zhou, Yijing, Bosch, Daniëlle G.M., van Haaften, Gijs, Granger, Leslie, Petersen, Andrea K., Pérez-Jurado, Luis A., Aznar-Laín, Gemma, Aneja, Anushree, Hancarova, Miroslava, Bendova, Sarka, Schwarz, Martin, Pourova, Radka Kremlikova, Sedlacek, Zdenek, Keena, Beth A., March, Michael E., Hou, Cuiping, O’Connor, Nora, Bhoj, Elizabeth J., Harr, Margaret H., Lemire, Gabrielle, Boycott, Kym M., Towne, Meghan, Li, Megan, Tarnopolsky, Mark, Brady, Lauren, Parker, Michael J., Faghfoury, Hanna, Parsley, Lea Kristin, Agolini, Emanuele, Dentici, Maria Lisa, Novelli, Antonio, Wright, Meredith, Palmquist, Rachel, Lai, Khanh, Scala, Marcello, Striano, Pasquale, Iacomino, Michele, Zara, Federico, Cooper, Annina, Maarup, Timothy J., Byler, Melissa, Lebel, Robert Roger, Balci, Tugce B., Louie, Raymond, Lyons, Michael, Douglas, Jessica, Nowak, Catherine, Afenjar, Alexandra, Hoyer, Juliane, Keren, Boris, Maas, Saskia M., Motazacker, Mahdi M., Martinez-Agosto, Julian A., Rabani, Ahna M., McCormick, Elizabeth M., Falk, Marni J., Ruggiero, Sarah M., Helbig, Ingo, Møller, Rikke S., Tessarollo, Lino, Ardori, Francesco Tomassoni, Palko, Mary Ellen, Hsieh, Tzung Chien, Krawitz, Peter M., Ganapathi, Mythily, Gelb, Bruce D., Jobanputra, Vaidehi, Wilson, Ashley, Greally, John, Jacquemont, Sébastien, Jizi, Khadijé, Bruel, Ange Line, Quelin, Chloé, Misra, Vinod K., Chick, Erika, Romano, Corrado, Greco, Donatella, Arena, Alessia, Morleo, Manuela, Nigro, Vincenzo, Seyama, Rie, Uchiyama, Yuri, Matsumoto, Naomichi, Taira, Ryoji, Tashiro, Katsuya, Sakai, Yasunari, Yigit, Gökhan, Wollnik, Bernd, Wagner, Michael, Kutsche, Barbara, Hurst, Anna C.E., Thompson, Michelle L., Schmidt, Ryan, Randolph, Linda, Spillmann, Rebecca C., Shashi, Vandana, Higginbotham, Edward J., Cordeiro, Dawn, Carnevale, Amanda, Costain, Gregory, Khan, Tayyaba, Funalot, Benoît, Mau-Them, Frederic Tran, Garcia Moya, Luis Fernandez, García-Miñaúr, Sixto, Osmond, Matthew, Chad, Lauren, Quercia, Nada, Carrasco, Diana, Li, Chumei, Sanchez-Valle, Amarilis, Kelley, Meghan, Nizon, Mathilde, Jensson, Brynjar O., Sulem, Patrick, Stefansson, Kari, Gorokhova, Svetlana, Busa, Tiffany, Rio, Marlène, Habdallah, Hamza Hadj, Lesieur-Sebellin, Marion, Amiel, Jeanne, Pingault, Véronique, Mercier, Sandra, Vincent, Marie, Philippe, Christophe, Fatus-Fauconnier, Clemence, Friend, Kathryn, Halligan, Rebecca K., Biswas, Sunita, Rosser, Jane, Shoubridge, Cheryl, Corbett, Mark, Barnett, Christopher, Gecz, Jozef, Leppig, Kathleen, Slavotinek, Anne, Marcelis, Carlo, Pfundt, Rolph, de Vries, Bert B.A., van Slegtenhorst, Marjon A., Brooks, Alice S., Cogne, Benjamin, Rambaud, Thomas, Tümer, Zeynep, Zackai, Elaine H., Akizu, Naiara, Song, Yuanquan, Hakonarson, Hakon, Genetica, Genetica Sectie Research, Cancer, Child Health, Li, Dong, Wang, Qin, Bayat, Allan, Battig, Mark R., Zhou, Yijing, Bosch, Daniëlle G.M., van Haaften, Gijs, Granger, Leslie, Petersen, Andrea K., Pérez-Jurado, Luis A., Aznar-Laín, Gemma, Aneja, Anushree, Hancarova, Miroslava, Bendova, Sarka, Schwarz, Martin, Pourova, Radka Kremlikova, Sedlacek, Zdenek, Keena, Beth A., March, Michael E., Hou, Cuiping, O’Connor, Nora, Bhoj, Elizabeth J., Harr, Margaret H., Lemire, Gabrielle, Boycott, Kym M., Towne, Meghan, Li, Megan, Tarnopolsky, Mark, Brady, Lauren, Parker, Michael J., Faghfoury, Hanna, Parsley, Lea Kristin, Agolini, Emanuele, Dentici, Maria Lisa, Novelli, Antonio, Wright, Meredith, Palmquist, Rachel, Lai, Khanh, Scala, Marcello, Striano, Pasquale, Iacomino, Michele, Zara, Federico, Cooper, Annina, Maarup, Timothy J., Byler, Melissa, Lebel, Robert Roger, Balci, Tugce B., Louie, Raymond, Lyons, Michael, Douglas, Jessica, Nowak, Catherine, Afenjar, Alexandra, Hoyer, Juliane, Keren, Boris, Maas, Saskia M., Motazacker, Mahdi M., Martinez-Agosto, Julian A., Rabani, Ahna M., McCormick, Elizabeth M., Falk, Marni J., Ruggiero, Sarah M., Helbig, Ingo, Møller, Rikke S., Tessarollo, Lino, Ardori, Francesco Tomassoni, Palko, Mary Ellen, Hsieh, Tzung Chien, Krawitz, Peter M., Ganapathi, Mythily, Gelb, Bruce D., Jobanputra, Vaidehi, Wilson, Ashley, Greally, John, Jacquemont, Sébastien, Jizi, Khadijé, Bruel, Ange Line, Quelin, Chloé, Misra, Vinod K., Chick, Erika, Romano, Corrado, Greco, Donatella, Arena, Alessia, Morleo, Manuela, Nigro, Vincenzo, Seyama, Rie, Uchiyama, Yuri, Matsumoto, Naomichi, Taira, Ryoji, Tashiro, Katsuya, Sakai, Yasunari, Yigit, Gökhan, Wollnik, Bernd, Wagner, Michael, Kutsche, Barbara, Hurst, Anna C.E., Thompson, Michelle L., Schmidt, Ryan, Randolph, Linda, Spillmann, Rebecca C., Shashi, Vandana, Higginbotham, Edward J., Cordeiro, Dawn, Carnevale, Amanda, Costain, Gregory, Khan, Tayyaba, Funalot, Benoît, Mau-Them, Frederic Tran, Garcia Moya, Luis Fernandez, García-Miñaúr, Sixto, Osmond, Matthew, Chad, Lauren, Quercia, Nada, Carrasco, Diana, Li, Chumei, Sanchez-Valle, Amarilis, Kelley, Meghan, Nizon, Mathilde, Jensson, Brynjar O., Sulem, Patrick, Stefansson, Kari, Gorokhova, Svetlana, Busa, Tiffany, Rio, Marlène, Habdallah, Hamza Hadj, Lesieur-Sebellin, Marion, Amiel, Jeanne, Pingault, Véronique, Mercier, Sandra, Vincent, Marie, Philippe, Christophe, Fatus-Fauconnier, Clemence, Friend, Kathryn, Halligan, Rebecca K., Biswas, Sunita, Rosser, Jane, Shoubridge, Cheryl, Corbett, Mark, Barnett, Christopher, Gecz, Jozef, Leppig, Kathleen, Slavotinek, Anne, Marcelis, Carlo, Pfundt, Rolph, de Vries, Bert B.A., van Slegtenhorst, Marjon A., Brooks, Alice S., Cogne, Benjamin, Rambaud, Thomas, Tümer, Zeynep, Zackai, Elaine H., Akizu, Naiara, Song, Yuanquan, and Hakonarson, Hakon

Published
2024

47. GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

Author

Hsieh, Tzung-Chien, Lesmann, Hellen, Hustinx, Alexander, Moosa, Shahida, Marchi, Elaine, Martin, Maria Del Pilar Caro, Abdelrazek, Ibrahim, Pantel, Jean Tori, Klinkhammer, Hannah, Hagen, Merle Ten, Thong, Meow-Keong, Mazlan, Rifhan Azwani Binti, Tae, Sok Kun, Kamphans, Tom, Meiswinkel, Wolfgang, Javanmardi, Behnam, Knaus, Alexej, Uwineza, Annette, Knopp, Cordula, Tkemaladze, Tinatin, Elbracht, Miriam, Mattern, Larissa, Jamra, Rami Abou, Velmans, Clara, Strehlow, Vincent, Jacob, Maureen, Peron, Angela, Dias, Cristina, Nunes, Beatriz, Vilella, Thainá, Pinheiro, Isabel, Kim, Chong, Melaragno, Maria, Weiland, Hannah, Kaptain, Sophia, Chwiałkowska, Karolina, Kwasniewski, Miroslaw, Saad, Ramy, Wiethoff, Sarah, Goel, Himanshu, Tang, Clara, Hau, Anna, Barakat, Tahsin Stefan, Panek, Przemysław, Nabil, Amira, Suh, Julia, Braun, Frederik, Gomy, Israel, Averdunk, Luisa, Ekure, Ekanem, Bergant, Gaber, Peterlin, Borut, Graziano, Claudio, Gaboon, Nagwa, Fiesco-Roa, Moisés, Spinelli, Alessandro, Wilpert, Nina-Maria, Phowthongkum, Prasit, Güzel, Nergis, Haack, Tobias, Bitar, Rana, Tzschach, Andreas, Rodriguez-Palmero, Agusti, Brunet, Theresa, Rudnik-Schöneborn, Sabine, Contreras-Capetillo, Silvina, Oberlack, Ava, Samango-Sprouse, Carole, Sadeghin, Teresa, Olaya, Margaret, Platzer, Konrad, Borovikov, Artem, Schnabel, Franziska, Heuft, Lara, Herrmann, Vera, Elkhateeb, Nour, Kumar, Sheetal, Komlosi, Katalin, Mohamed, Khoushoua, Kalantari, Silvia, Sirchia, Fabio, Martinez-Monseny, Antonio, Höller, Matthias, Mohamed, Amal, Lasa-Aranzasti, Amaia, Sayer, John, Ehmke, Nadja, Danyel, Magdalena, Sczakiel, Henrike, Schwartzmann, Sarina, Boschann, Felix, Zhao, Max, Adams, Ronja, Einicke, Lara, Chew, Kee Seang, Kam, Choy Chen, Karakoyun, Miray, Pode-Shakked, Ben, Eliyahu, Aviva, Rock, Rachel, Carrion, Teresa, Chorin, Odelia, Zarate, Yuri, Martinez, Marcello, Karakaya, Mert, Tung, Moon Ley, Chandra, Bharatendu, Lumaka, Aimé, Shinawi, Marwan, Blackburn, Patrick, Wang, Tianyun, Niehues, Tim, Hu, Ping, Waikel, Rebekah, Hanchard, Suzanna Ledgister, Elmakkawy, Gehad, Safwat, Sylvia, Ebstein, Frédéric, Krüger, Elke, Küry, Sébastien, Bezieau, Stephane, Arlt, Annabelle, Marbach, Felix, Li, Dong, Dupuis, Lucie, Mendoza-Londono, Roberto, Houge, Sofia Douzgou, Weis, Denisa, Mak, Christopher, Elcioglu, Nursel, Aykut, Ayca, Şimşek-Kiper, Peli, Bögershausen, Nina, Wollnik, Bernd, Bentzen, Heidi Beate, Kurth, Ingo, Netzer, Christian, Jezela-Stanek, Aleksandra, Devriendt, Koen, Gripp, Karen, Mücke, Martin, Verloes, Alain, Schaaf, Christian, Nellåker, Christoffer, Solomon, Benjamin, Nöthen, Markus, Abdalla, Ebtesam, Lyon, Gholson, Krawitz, Peter, Kayserili, Hulya, Toutouna, Louiza, Schmidt, Axel, Roth, Regina, Wieczorek, Dagmar, Olinger, Eric, Hsieh, Tzung-Chien, Lesmann, Hellen, Hustinx, Alexander, Moosa, Shahida, Marchi, Elaine, Martin, Maria Del Pilar Caro, Abdelrazek, Ibrahim, Pantel, Jean Tori, Klinkhammer, Hannah, Hagen, Merle Ten, Thong, Meow-Keong, Mazlan, Rifhan Azwani Binti, Tae, Sok Kun, Kamphans, Tom, Meiswinkel, Wolfgang, Javanmardi, Behnam, Knaus, Alexej, Uwineza, Annette, Knopp, Cordula, Tkemaladze, Tinatin, Elbracht, Miriam, Mattern, Larissa, Jamra, Rami Abou, Velmans, Clara, Strehlow, Vincent, Jacob, Maureen, Peron, Angela, Dias, Cristina, Nunes, Beatriz, Vilella, Thainá, Pinheiro, Isabel, Kim, Chong, Melaragno, Maria, Weiland, Hannah, Kaptain, Sophia, Chwiałkowska, Karolina, Kwasniewski, Miroslaw, Saad, Ramy, Wiethoff, Sarah, Goel, Himanshu, Tang, Clara, Hau, Anna, Barakat, Tahsin Stefan, Panek, Przemysław, Nabil, Amira, Suh, Julia, Braun, Frederik, Gomy, Israel, Averdunk, Luisa, Ekure, Ekanem, Bergant, Gaber, Peterlin, Borut, Graziano, Claudio, Gaboon, Nagwa, Fiesco-Roa, Moisés, Spinelli, Alessandro, Wilpert, Nina-Maria, Phowthongkum, Prasit, Güzel, Nergis, Haack, Tobias, Bitar, Rana, Tzschach, Andreas, Rodriguez-Palmero, Agusti, Brunet, Theresa, Rudnik-Schöneborn, Sabine, Contreras-Capetillo, Silvina, Oberlack, Ava, Samango-Sprouse, Carole, Sadeghin, Teresa, Olaya, Margaret, Platzer, Konrad, Borovikov, Artem, Schnabel, Franziska, Heuft, Lara, Herrmann, Vera, Elkhateeb, Nour, Kumar, Sheetal, Komlosi, Katalin, Mohamed, Khoushoua, Kalantari, Silvia, Sirchia, Fabio, Martinez-Monseny, Antonio, Höller, Matthias, Mohamed, Amal, Lasa-Aranzasti, Amaia, Sayer, John, Ehmke, Nadja, Danyel, Magdalena, Sczakiel, Henrike, Schwartzmann, Sarina, Boschann, Felix, Zhao, Max, Adams, Ronja, Einicke, Lara, Chew, Kee Seang, Kam, Choy Chen, Karakoyun, Miray, Pode-Shakked, Ben, Eliyahu, Aviva, Rock, Rachel, Carrion, Teresa, Chorin, Odelia, Zarate, Yuri, Martinez, Marcello, Karakaya, Mert, Tung, Moon Ley, Chandra, Bharatendu, Lumaka, Aimé, Shinawi, Marwan, Blackburn, Patrick, Wang, Tianyun, Niehues, Tim, Hu, Ping, Waikel, Rebekah, Hanchard, Suzanna Ledgister, Elmakkawy, Gehad, Safwat, Sylvia, Ebstein, Frédéric, Krüger, Elke, Küry, Sébastien, Bezieau, Stephane, Arlt, Annabelle, Marbach, Felix, Li, Dong, Dupuis, Lucie, Mendoza-Londono, Roberto, Houge, Sofia Douzgou, Weis, Denisa, Mak, Christopher, Elcioglu, Nursel, Aykut, Ayca, Şimşek-Kiper, Peli, Bögershausen, Nina, Wollnik, Bernd, Bentzen, Heidi Beate, Kurth, Ingo, Netzer, Christian, Jezela-Stanek, Aleksandra, Devriendt, Koen, Gripp, Karen, Mücke, Martin, Verloes, Alain, Schaaf, Christian, Nellåker, Christoffer, Solomon, Benjamin, Nöthen, Markus, Abdalla, Ebtesam, Lyon, Gholson, Krawitz, Peter, Kayserili, Hulya, Toutouna, Louiza, Schmidt, Axel, Roth, Regina, Wieczorek, Dagmar, and Olinger, Eric

Abstract

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB wi

Published
2024

48. Spliceosome malfunction causes neurodevelopmental disorders with overlapping features

Author

Li, Dong, Wang, Qin, Bayat, Allan, Battig, Mark R., Zhou, Yijing, Bosch, Daniëlle G.M., van Haaften, Gijs, Granger, Leslie, Petersen, Andrea K., Pérez-Jurado, Luis A., Aznar-Laín, Gemma, Aneja, Anushree, Hancarova, Miroslava, Bendova, Sarka, Schwarz, Martin, Pourova, Radka Kremlikova, Sedlacek, Zdenek, Keena, Beth A., March, Michael E., Hou, Cuiping, O’Connor, Nora, Bhoj, Elizabeth J., Harr, Margaret H., Lemire, Gabrielle, Boycott, Kym M., Towne, Meghan, Li, Megan, Tarnopolsky, Mark, Brady, Lauren, Parker, Michael J., Faghfoury, Hanna, Parsley, Lea Kristin, Agolini, Emanuele, Dentici, Maria Lisa, Novelli, Antonio, Wright, Meredith, Palmquist, Rachel, Lai, Khanh, Scala, Marcello, Striano, Pasquale, Iacomino, Michele, Zara, Federico, Cooper, Annina, Maarup, Timothy J., Byler, Melissa, Lebel, Robert Roger, Balci, Tugce B., Louie, Raymond, Lyons, Michael, Douglas, Jessica, Nowak, Catherine, Afenjar, Alexandra, Hoyer, Juliane, Keren, Boris, Maas, Saskia M., Motazacker, Mahdi M., Martinez-Agosto, Julian A., Rabani, Ahna M., McCormick, Elizabeth M., Falk, Marni J., Ruggiero, Sarah M., Helbig, Ingo, Møller, Rikke S., Tessarollo, Lino, Ardori, Francesco Tomassoni, Palko, Mary Ellen, Hsieh, Tzung Chien, Krawitz, Peter M., Ganapathi, Mythily, Gelb, Bruce D., Jobanputra, Vaidehi, Wilson, Ashley, Greally, John, Jacquemont, Sébastien, Jizi, Khadijé, Bruel, Ange Line, Quelin, Chloé, Misra, Vinod K., Chick, Erika, Romano, Corrado, Greco, Donatella, Arena, Alessia, Morleo, Manuela, Nigro, Vincenzo, Seyama, Rie, Uchiyama, Yuri, Matsumoto, Naomichi, Taira, Ryoji, Tashiro, Katsuya, Sakai, Yasunari, Yigit, Gökhan, Wollnik, Bernd, Wagner, Michael, Kutsche, Barbara, Hurst, Anna C.E., Thompson, Michelle L., Schmidt, Ryan, Randolph, Linda, Spillmann, Rebecca C., Shashi, Vandana, Higginbotham, Edward J., Cordeiro, Dawn, Carnevale, Amanda, Costain, Gregory, Khan, Tayyaba, Funalot, Benoît, Mau-Them, Frederic Tran, Garcia Moya, Luis Fernandez, García-Miñaúr, Sixto, Osmond, Matthew, Chad, Lauren, Quercia, Nada, Carrasco, Diana, Li, Chumei, Sanchez-Valle, Amarilis, Kelley, Meghan, Nizon, Mathilde, Jensson, Brynjar O., Sulem, Patrick, Stefansson, Kari, Gorokhova, Svetlana, Busa, Tiffany, Rio, Marlène, Habdallah, Hamza Hadj, Lesieur-Sebellin, Marion, Amiel, Jeanne, Pingault, Véronique, Mercier, Sandra, Vincent, Marie, Philippe, Christophe, Fatus-Fauconnier, Clemence, Friend, Kathryn, Halligan, Rebecca K., Biswas, Sunita, Rosser, Jane, Shoubridge, Cheryl, Corbett, Mark, Barnett, Christopher, Gecz, Jozef, Leppig, Kathleen, Slavotinek, Anne, Marcelis, Carlo, Pfundt, Rolph, de Vries, Bert B.A., van Slegtenhorst, Marjon A., Brooks, Alice S., Cogne, Benjamin, Rambaud, Thomas, Tümer, Zeynep, Zackai, Elaine H., Akizu, Naiara, Song, Yuanquan, Hakonarson, Hakon, Li, Dong, Wang, Qin, Bayat, Allan, Battig, Mark R., Zhou, Yijing, Bosch, Daniëlle G.M., van Haaften, Gijs, Granger, Leslie, Petersen, Andrea K., Pérez-Jurado, Luis A., Aznar-Laín, Gemma, Aneja, Anushree, Hancarova, Miroslava, Bendova, Sarka, Schwarz, Martin, Pourova, Radka Kremlikova, Sedlacek, Zdenek, Keena, Beth A., March, Michael E., Hou, Cuiping, O’Connor, Nora, Bhoj, Elizabeth J., Harr, Margaret H., Lemire, Gabrielle, Boycott, Kym M., Towne, Meghan, Li, Megan, Tarnopolsky, Mark, Brady, Lauren, Parker, Michael J., Faghfoury, Hanna, Parsley, Lea Kristin, Agolini, Emanuele, Dentici, Maria Lisa, Novelli, Antonio, Wright, Meredith, Palmquist, Rachel, Lai, Khanh, Scala, Marcello, Striano, Pasquale, Iacomino, Michele, Zara, Federico, Cooper, Annina, Maarup, Timothy J., Byler, Melissa, Lebel, Robert Roger, Balci, Tugce B., Louie, Raymond, Lyons, Michael, Douglas, Jessica, Nowak, Catherine, Afenjar, Alexandra, Hoyer, Juliane, Keren, Boris, Maas, Saskia M., Motazacker, Mahdi M., Martinez-Agosto, Julian A., Rabani, Ahna M., McCormick, Elizabeth M., Falk, Marni J., Ruggiero, Sarah M., Helbig, Ingo, Møller, Rikke S., Tessarollo, Lino, Ardori, Francesco Tomassoni, Palko, Mary Ellen, Hsieh, Tzung Chien, Krawitz, Peter M., Ganapathi, Mythily, Gelb, Bruce D., Jobanputra, Vaidehi, Wilson, Ashley, Greally, John, Jacquemont, Sébastien, Jizi, Khadijé, Bruel, Ange Line, Quelin, Chloé, Misra, Vinod K., Chick, Erika, Romano, Corrado, Greco, Donatella, Arena, Alessia, Morleo, Manuela, Nigro, Vincenzo, Seyama, Rie, Uchiyama, Yuri, Matsumoto, Naomichi, Taira, Ryoji, Tashiro, Katsuya, Sakai, Yasunari, Yigit, Gökhan, Wollnik, Bernd, Wagner, Michael, Kutsche, Barbara, Hurst, Anna C.E., Thompson, Michelle L., Schmidt, Ryan, Randolph, Linda, Spillmann, Rebecca C., Shashi, Vandana, Higginbotham, Edward J., Cordeiro, Dawn, Carnevale, Amanda, Costain, Gregory, Khan, Tayyaba, Funalot, Benoît, Mau-Them, Frederic Tran, Garcia Moya, Luis Fernandez, García-Miñaúr, Sixto, Osmond, Matthew, Chad, Lauren, Quercia, Nada, Carrasco, Diana, Li, Chumei, Sanchez-Valle, Amarilis, Kelley, Meghan, Nizon, Mathilde, Jensson, Brynjar O., Sulem, Patrick, Stefansson, Kari, Gorokhova, Svetlana, Busa, Tiffany, Rio, Marlène, Habdallah, Hamza Hadj, Lesieur-Sebellin, Marion, Amiel, Jeanne, Pingault, Véronique, Mercier, Sandra, Vincent, Marie, Philippe, Christophe, Fatus-Fauconnier, Clemence, Friend, Kathryn, Halligan, Rebecca K., Biswas, Sunita, Rosser, Jane, Shoubridge, Cheryl, Corbett, Mark, Barnett, Christopher, Gecz, Jozef, Leppig, Kathleen, Slavotinek, Anne, Marcelis, Carlo, Pfundt, Rolph, de Vries, Bert B.A., van Slegtenhorst, Marjon A., Brooks, Alice S., Cogne, Benjamin, Rambaud, Thomas, Tümer, Zeynep, Zackai, Elaine H., Akizu, Naiara, Song, Yuanquan, and Hakonarson, Hakon

Abstract

Pre-mRNA splicing is a highly coordinated process. While its dysregulation has been linked to neurological deficits, our understanding of the underlying molecular and cellular mechanisms remains limited. We implicated pathogenic variants in U2AF2 and PRPF19, encoding spliceosome subunits in neurodevelopmental disorders (NDDs), by identifying 46 unrelated individuals with 23 de novo U2AF2 missense variants (including 7 recurrent variants in 30 individuals) and 6 individuals with de novo PRPF19 variants. Eight U2AF2 variants dysregulated splicing of a model substrate. Neuritogenesis was reduced in human neurons differentiated from human pluripotent stem cells carrying two U2AF2 hyper-recurrent variants. Neural loss of function (LoF) of the Drosophila orthologs U2af50 and Prp19 led to lethality, abnormal mushroom body (MB) patterning, and social deficits, which were differentially rescued by wild-type and mutant U2AF2 or PRPF19. Transcriptome profiling revealed splicing substrates or effectors (including Rbfox1, a third splicing factor), which rescued MB defects in U2af50deficient flies. Upon reanalysis of negative clinical exomes followed by data sharing, we further identified 6 patients with NDD who carried RBFOX1 missense variants which, by in vitro testing, showed LoF. Our study implicates 3 splicing factors as NDD-causative genes and establishes a genetic network with hierarchy underlying human brain development and function.

Published
2024

49. Impact of Synbiotic Intake on Liver Metabolism in Metabolically Healthy Participants and Its Potential Preventive Effect on Metabolic-Dysfunction-Associated Fatty Liver Disease (MAFLD):A Randomized, Placebo-Controlled, Double-Blinded Clinical Trial

Author

Mantri, Aakash, Köhlmoos, Anika, Schelski, Daniela Stephanie, Seel, Waldemar, Stoffel-Wagner, Birgit, Krawitz, Peter, Stehle, Peter, Holst, Jens Juul, Weber, Bernd, Koban, Leonie, Plassmann, Hilke, Simon, Marie Christine, Mantri, Aakash, Köhlmoos, Anika, Schelski, Daniela Stephanie, Seel, Waldemar, Stoffel-Wagner, Birgit, Krawitz, Peter, Stehle, Peter, Holst, Jens Juul, Weber, Bernd, Koban, Leonie, Plassmann, Hilke, and Simon, Marie Christine

Abstract

Synbiotics modulate the gut microbiome and contribute to the prevention of liver diseases such as metabolic-dysfunction-associated fatty liver disease (MAFLD). This study aimed to evaluate the effect of a randomized, placebo-controlled, double-blinded seven-week intervention trial on the liver metabolism in 117 metabolically healthy male participants. Anthropometric data, blood parameters, and stool samples were analyzed using linear mixed models. After seven weeks of intervention, there was a significant reduction in alanine aminotransferase (ALT) in the synbiotic group compared to the placebo group (-14.92%, CI: -26.60--3.23%, p = 0.013). A stratified analysis according to body fat percentage revealed a significant decrease in ALT (-20.70%, CI: -40.88--0.53%, p = 0.045) in participants with an elevated body fat percentage. Further, a significant change in microbiome composition (1.16, CI: 0.06-2.25, p = 0.039) in this group was found, while the microbial composition remained stable upon intervention in the group with physiological body fat. The 7-week synbiotic intervention reduced ALT levels, especially in participants with an elevated body fat percentage, possibly due to modulation of the gut microbiome. Synbiotic intake may be helpful in delaying the progression of MAFLD and could be used in addition to the recommended lifestyle modification therapy.

Published
2024

50. Approximating facial expression effects on diagnostic accuracy via generative AI in medical genetics.

Author

Patel, Tanviben, Othman, Amna A, Sümer, Ömer, Hellman, Fabio, Krawitz, Peter, André, Elisabeth, Ripper, Molly E, Fortney, Chris, Persky, Susan, Hu, Ping, Tekendo-Ngongang, Cedrik, Hanchard, Suzanna Ledgister, Flaharty, Kendall A, Waikel, Rebekah L, Duong, Dat, and Solomon, Benjamin D

Subjects
GENERATIVE artificial intelligence, FACIAL expression, GENE expression, MEDICAL genetics, ARTIFICIAL intelligence
Abstract

Summary Artificial intelligence (AI) is increasingly used in genomics research and practice, and generative AI has garnered significant recent attention. In clinical applications of generative AI, aspects of the underlying datasets can impact results, and confounders should be studied and mitigated. One example involves the facial expressions of people with genetic conditions. Stereotypically, Williams (WS) and Angelman (AS) syndromes are associated with a "happy" demeanor, including a smiling expression. Clinical geneticists may be more likely to identify these conditions in images of smiling individuals. To study the impact of facial expression, we analyzed publicly available facial images of approximately 3500 individuals with genetic conditions. Using a deep learning (DL) image classifier, we found that WS and AS images with non-smiling expressions had significantly lower prediction probabilities for the correct syndrome labels than those with smiling expressions. This was not seen for 22q11.2 deletion and Noonan syndromes, which are not associated with a smiling expression. To further explore the effect of facial expressions, we computationally altered the facial expressions for these images. We trained HyperStyle, a GAN-inversion technique compatible with StyleGAN2, to determine the vector representations of our images. Then, following the concept of InterfaceGAN, we edited these vectors to recreate the original images in a phenotypically accurate way but with a different facial expression. Through online surveys and an eye-tracking experiment, we examined how altered facial expressions affect the performance of human experts. We overall found that facial expression is associated with diagnostic accuracy variably in different genetic conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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