Search

Your search keyword '"Lindstrand, Anna"' showing total 7 results
Start Over Author "Lindstrand, Anna" Topic genomics
7 results on '"Lindstrand, Anna"'

2. Solving patients with rare diseases through programmatic reanalysis of genome-phenome data

Author

Matalonga, Leslie, Hernández-Ferrer, Carles, DITF-ITHACA, Solve-RD, Verloes, Alain, Vissers, Lisenka, Vitobello, Antonio, Votypka, Pavel, Vyshka, Klea, Zurek, Birte, Baets, Jonathan, Beijer, Danique, Bonne, Gisèle, Cohen, Enzo, DITF-euroNMD, Solve-RD, Cossins, Judith, Evangelista, Teresinha, Ferlini, Alessandra, Hackman, Peter, Hanna, Michael G, Horvath, Rita, Houlden, Henry, Johari, Mridul, Lau, Jarred, Lochmüller, Hanns, DITF-RND, Solve-RD, Macken, William L, Musacchia, Francesco, Nascimento, Andres, Natera-de Benito, Daniel, Nigro, Vincenzo, Piluso, Giulio, Pini, Veronica, Pitceathly, Robert D S, Polavarapu, Kiran, Cruz, Pedro M Rodriguez, Tonda, Raul, Sarkozy, Anna, Savarese, Marco, Selvatici, Rita, Thompson, Rachel, Udd, Bjarne, Van de Vondel, Liedewei, Vandrovcova, Jana, Zaharieva, Irina, Balicza, Peter, Laurie, Steven, Chinnery, Patrick, Dürr, Alexandra, Haack, Tobias, Hengel, Holger, Kamsteeg, Erik-Jan, Kamsteeg, Christoph, Lohmann, Katja, Macaya, Alfons, Marcé-Grau, Anna, Fernandez-Callejo, Marcos, Maver, Ales, Molnar, Judit, Münchau, Alexander, Peterlin, Borut, Riess, Olaf, Schöls, Ludger, Schüle-Freyer, Rebecca, Stevanin, Giovanni, Synofzik, Matthis, Timmerman, Vincent, Picó, Daniel, van de Warrenburg, Bart, van Os, Nienke, Wayand, Melanie, Wilke, Carlo, Haack, Tobias B, Graessner, Holm, Ellwanger, Kornelia, Ossowski, Stephan, Demidov, German, Garcia-Linares, Carles, Sturm, Marc, Schulze-Hentrich, Julia M, Kessler, Christoph, Heutink, Peter, Brunner, Han, Scheffer, Hans, Papakonstantinou, Anastasios, Hoogerbrugge, Nicoline, 't Hoen, Peter A C, Steyaert, Wouter, Sablauskas, Karolis, Te Paske, Iris, Janssen, Erik, Steehouwer, Marloes, Yaldiz, Burcu, Corvó, Alberto, Brookes, Anthony J, Veal, Colin, Gibson, Spencer, Wadsley, Marc, Mehtarizadeh, Mehdi, Riaz, Umar, Warren, Greg, Dizjikan, Farid Yavari, Shorter, Thomas, Straub, Volker, Piscia, Davide, Joshi, Ricky, Bettolo, Chiara Marini, Specht, Sabine, Clayton-Smith, Jill, Banka, Siddharth, Alexander, Elizabeth, Jackson, Adam, Faivre, Laurence, Thauvin, Christel, Duffourd, Yannis, Tisserant, Emilie, Diez, Hector, Bruel, Ange-Line, Peyron, Christine, Pélissier, Aurore, Beltran, Sergi, Gut, Ivo Glynne, Bullich, Gemma, Gut, Ivo, Corvo, Alberto, Garcia, Carles, Hernández, Carles, Paramonov, Ida, Gumus, Gulcin, Bros-Facer, Virginie, Rath, Ana, Hoischen, Alexander, Hanauer, Marc, Olry, Annie, Lagorce, David, Havrylenko, Svitlana, Izem, Katia, Rigour, Fanny, Davoine, Claire-Sophie, Guillot-Noel, Léna, Heinzmann, Anna, Coarelli, Giulia, Allamand, Valérie, Nelson, Isabelle, Yaou, Rabah Ben, Metay, Corinne, Eymard, Bruno, Atalaia, Antonio, Stojkovic, Tanya, Macek, Milan, Turnovec, Marek, Thomasová, Dana, Kremliková, Radka Pourová, Franková, Vera, Havlovicová, Markéta, Kremlik, Vlastimil, Parkinson, Helen, Keane, Thomas, Consortia, Solve-RD, Spalding, Dylan, Senf, Alexander, Danis, Daniel, Robert, Glenn, Costa, Alessia, Patch, Christine, Hanna, Mike, Reilly, Mary, Muntoni, Francesco, de Jonghe, Peter, Banfi, Sandro, Torella, Annalaura, Cuesta, Isabel, Rossi, Rachele, Neri, Marcella, Aretz, Stefan, Spier, Isabel, Peters, Sophia, Oliveira, Carla, Pelaez, Jose Garcia, Matos, Ana Rita, José, Celina São, Ferreira, Marta, Gullo, Irene, Fernandes, Susana, Garrido, Luzia, Ferreira, Pedro, Carneiro, Fátima, Swertz, Morris A, Johansson, Lennart, van der Vries, Gerben, Neerincx, Pieter B, group, Solve-RD SNV-indel working, Denommé-Pichon, Anne-Sophie, Roelofs-Prins, Dieuwke, Köhler, Sebastian, Metcalfe, Alison, Rooryck, Caroline, Trimouille, Aurelien, Castello, Raffaele, Morleo, Manuela, Varavallo, Alessandra, De la Paz, Manuel Posada, Sánchez, Eva Bermejo, Martín, Estrella López, Delgado, Beatriz Martínez, de la Rosa, F Javier Alonso García, Radio, Francesca Clementina, Tartaglia, Marco, Renieri, Alessandra, Benetti, Elisa, Molnar, Maria Judit, Gilissen, Christian, Herzog, Rebecca, Pauly, Martje, Osorio, Andres Nascimiento, de Benito, Daniel Natera, Beeson, David, Capella, Gabriel, Valle, Laura, Holinski-Feder, Elke, Laner, Andreas, Steinke-Lange, Verena, Schröck, Evelin, Rump, Andreas, Li, Shuang, Prasanth, Sivakumar, Robinson, Peter, van der Velde, Joeri K, de Voer, Richarda M, Evans, Gareth, Sommer, Anna Katharina, Töpf, Ana, Paske, Iris Te, Tischkowitz, Marc, Casari, Giorgio, Ciolfi, Andrea, Dallapiccola, Bruno, de Boer, Elke, Vissers, Lisenka E L M, Hammarsjö, Anna, Havlovicova, Marketa, Hugon, Anne, de Voer, Richarda, Kleefstra, Tjitske, Lindstrand, Anna, López-Martín, Estrella, Nigro, Vicenzo, Nordgren, Ann, Pettersson, Maria, Pinelli, Michele, Pizzi, Simone, DITF-GENTURIS, Solve-RD, Posada, Manuel, Ryba, Lukas, Schwarz, Martin, Trimouille, Aurélien, Solve RD SNV Indel Working Grp, Solve RD DITF GENTURIS, Solve RD DITF ITHACA, Solve RD DITF-euroNMD, Solve RD DITF RND, Solve RD Consortia, Matalonga, L., Hernandez-Ferrer, C., Piscia, D., Cohen, E., Cuesta, I., Danis, D., Denomme-Pichon, A. -S., Duffourd, Y., Gilissen, C., Johari, M., Laurie, S., Li, S., Nelson, I., Peters, S., Paramonov, I., Prasanth, S., Robinson, P., Sablauskas, K., Savarese, M., Steyaert, W., van der Velde, J. K., Vitobello, A., Schule, R., Synofzik, M., Topf, A., Vissers, L. E. L. M., de Voer, R., Aretz, S., Capella, G., de Voer, R. M., Evans, G., Pelaez, J. G., Holinski-Feder, E., Hoogerbrugge, N., Laner, A., Oliveira, C., Rump, A., Schrock, E., Sommer, A. K., Steinke-Lange, V., Paske, I., Tischkowitz, M., Valle, L., Banka, S., Benetti, E., Casari, G., Ciolfi, A., Clayton-Smith, J., Dallapiccola, B., de Boer, E., Ellwanger, K., Faivre, L., Graessner, H., Haack, T. B., Hammarsjo, A., Havlovicova, M., Hoischen, A., Hugon, A., Jackson, A., Kleefstra, T., Lindstrand, A., Lopez-Martin, E., Macek, M., Morleo, M., Nigro, V., Nordgren, A., Pettersson, M., Pinelli, M., Pizzi, S., Posada, M., Radio, F. C., Renieri, A., Rooryck, C., Ryba, L., Schwarz, M., Tartaglia, M., Thauvin, C., Torella, A., Trimouille, A., Verloes, A., Vissers, L., Votypka, P., Vyshka, K., Zurek, B., Baets, J., Beijer, D., Bonne, G., Cossins, J., Evangelista, T., Ferlini, A., Hackman, P., Hanna, M. G., Horvath, R., Houlden, H., Lau, J., Lochmuller, H., Macken, W. L., Musacchia, F., Nascimento, A., Natera-de Benito, D., Piluso, G., Pini, V., Pitceathly, R. D. S., Polavarapu, K., Cruz, P. M. R., Sarkozy, A., Selvatici, R., Thompson, R., Udd, B., Van de Vondel, L., Vandrovcova, J., Zaharieva, I., Balicza, P., Chinnery, P., Durr, A., Haack, T., Hengel, H., Kamsteeg, E. -J., Kamsteeg, C., Lohmann, K., Macaya, A., Marce-Grau, A., Maver, A., Molnar, J., Munchau, A., Peterlin, B., Riess, O., Schols, L., Schule-Freyer, R., Stevanin, G., Timmerman, V., van de Warrenburg, B., van Os, N., Wayand, M., Wilke, C., Tonda, R., Fernandez-Callejo, M., Pico, D., Garcia-Linares, C., Papakonstantinou, A., Corvo, A., Joshi, R., Diez, H., Gut, I., Beltran, S., Ossowski, S., Demidov, G., Sturm, M., Schulze-Hentrich, J. M., Kessler, C., Heutink, P., Brunner, H., Scheffer, H., 't Hoen, P. A. C., te Paske, I., Janssen, E., Steehouwer, M., Yaldiz, B., Brookes, A. J., Veal, C., Gibson, S., Wadsley, M., Mehtarizadeh, M., Riaz, U., Warren, G., Dizjikan, F. Y., Shorter, T., Straub, V., Bettolo, C. M., Specht, S., Alexander, E., Tisserant, E., Bruel, A. -L., Peyron, C., Pelissier, A., Gut, I. G., Bullich, G., Garcia, C., Hernandez, C., Gumus, G., Bros-Facer, V., Rath, A., Hanauer, M., Olry, A., Lagorce, D., Havrylenko, S., Izem, K., Rigour, F., Davoine, C. -S., Guillot-Noel, L., Heinzmann, A., Coarelli, G., Allamand, V., Yaou, R. B., Metay, C., Eymard, B., Atalaia, A., Stojkovic, T., Turnovec, M., Thomasova, D., Kremlikova, R. P., Frankova, V., Kremlik, V., Parkinson, H., Keane, T., Spalding, D., Senf, A., Robert, G., Costa, A., Patch, C., Hanna, M., Reilly, M., Muntoni, F., de Jonghe, P., Banfi, S., Rossi, R., Neri, M., Spier, I., Matos, A. R., Jose, C. S., Ferreira, M., Gullo, I., Fernandes, S., Garrido, L., Ferreira, P., Carneiro, F., Swertz, M. A., Johansson, L., van der Vries, G., Neerincx, P. B., Roelofs-Prins, D., Kohler, S., Metcalfe, A., Castello, R., Varavallo, A., De la Paz, M. P., Sanchez, E. B., Martin, E. L., Delgado, B. M., de la Rosa, F. J. A. G., Molnar, M. J., Herzog, R., Pauly, M., Osorio, A. N., de Benito, D. N., Beeson, D., Unión Europea. Comisión Europea. H2020, Instituto de Salud Carlos III, Ministerio de Economía, Industria y Competitividad (España), Ministerio de Ciencia e Innovación. Centro de Excelencia Severo Ochoa (España), Government of Catalonia (España), Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF), Instituto Nacional de Bioinformatica (España), Klinische Genetica, RS: GROW - R4 - Reproductive and Perinatal Medicine, MUMC+: DA Klinische Genetica (5), Instituto de Salud Global - Institute For Global Health [Barcelona] (ISGlobal), Instituto de Salud Carlos III [Madrid] (ISC), Radboud University Medical Center [Nijmegen], Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Equipe GAD (LNC - U1231), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Barcelona Institute of Science and Technology (BIST), Centre de recherche en Myologie – U974 SU-INSERM, Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Folkhälsan Research Center, Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche en Myologie, University of Helsinki, Department of Medical and Clinical Genetics, Medicum, and Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI)

Subjects
Genetic testing, Computer science, genetics [Rare Diseases], lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], EXOME, MEDICAL GENETICS, Diseases, Disease, VARIANTS, Genome informatics, Genomic analysis, Diseases, Genetic testing, Genome informatics, Genomic analysis, Tumours of the digestive tract Radboud Institute for Molecular Life Sciences [Radboudumc 14], Exome, Genetics (clinical), Exome sequencing, 0303 health sciences, Application programming interface, methods [Genomics], 030305 genetics & heredity, 1184 Genetics, developmental biology, physiology, Genomics, Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3], 3. Good health, Pedigree, diagnosis [Rare Diseases], Chemistry, Medical genetics, medicine.medical_specialty, methods [Genetic Testing], MEDLINE, Socio-culturale, Phenome, AMERICAN-COLLEGE, INHERITANCE, Sensitivity and Specificity, Article, standards [Genetic Testing], 03 medical and health sciences, Rare Diseases, [SDV.MHEP.AHA]Life Sciences [q-bio]/Human health and pathology/Tissues and Organs [q-bio.TO], Genetics, medicine, Humans, ddc:610, Genetic Testing, Biology, 030304 developmental biology, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], Data science, Workflow, 3111 Biomedicine, standards [Genomics], Human medicine, Software
Abstract

Correction to: Solving patients with rare diseases through programmatic reanalysis of genome-phenome data. Eur J Hum Genet. 2021 Sep;29(9):1466-1469. doi: 10.1038/s41431-021-00934-6. PMID: 34393220 Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics. The Solve-RD project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779257. Data were analysed using the RD‐Connect Genome‐Phenome Analysis Platform, which received funding from EU projects RD‐Connect, Solve-RD and EJP-RD (grant numbers FP7 305444, H2020 779257, H2020 825575), Instituto de Salud Carlos III (grant numbers PT13/0001/0044, PT17/0009/0019; Instituto Nacional de Bioinformática, INB) and ELIXIR Implementation Studies. We acknowledge support of the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme/Generalitat de Catalunya. We also acknowledge the support of the Generalitat de Catalunya through Departament de Salut and Departament d’Empresa i Coneixement and the Co-financing by the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) with funds from the European Regional Development Fund (ERDF) corresponding to the 2014-2020 Smart Growth Operating Program. Sí

Published
2021
Full Text
View/download PDF

3. The value of age of onset and family history as predictors of molecular diagnosis in a Swedish cohort of inherited retinal disease.

Author

De Geer, Karl, Löfgren, Stefan, Lindstrand, Anna, Kvarnung, Malin, and Björck, Erik

Subjects
*AGE of onset, *MOLECULAR diagnosis, *RETINAL degeneration, *GENETIC testing, *RETINAL diseases
Abstract

Purpose Methods Results Conclusion This study aimed to characterize clinical and genetic findings in a Swedish cohort with inherited retinal disease (IRD), identify predictors for achieving a molecular diagnosis and evaluate the effects of increased genetic testing over time.Clinical and genetic data from 324 nonrelated IRD index individuals referred for genetic testing in the Stockholm region between 2016 and 2023 were collected retrospectively and analysed by clinical subtype, age of onset and testing period (2016–2020 vs. 2021–2023). Logistic regression was used to calculate odds ratios for age of onset and family history on the likelihood of achieving a molecular diagnosis.The diagnostic yield was 55% and involved 56 genes. In 10% of solved individuals, the molecular diagnosis refined the clinical diagnosis. For each 1‐year increase in age of onset, the odds of achieving a molecular diagnosis decreased by 3% (odds ratio 0.97, 95% confidence interval 0.96–0.98). A positive family history doubled the odds (odds ratio 2.1, 95% confidence interval 1.3–3.4). The use of genetic testing increased 2.1‐fold and the number of molecular diagnoses increased 1.6‐fold relative to the population of the Stockholm region between the two testing periods.This study adds to the knowledge of the clinical and genetic landscape of IRDs in Sweden and establishes age of onset and family history as significant predictors for achieving a molecular diagnosis. Increased genetic testing on a population level substantially increased the number of individuals receiving a molecular diagnosis with a high diagnostic yield compared to other rare diseases. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

4. Comprehensive structural variation genome map of individuals carrying complex chromosomal rearrangements.

Author

Eisfeldt, Jesper, Pettersson, Maria, Vezzi, Francesco, Wincent, Josephine, Käller, Max, Gruselius, Joel, Nilsson, Daniel, Syk Lundberg, Elisabeth, Carvalho, Claudia M. B., and Lindstrand, Anna

Subjects
CHROMOSOMES, NUCLEOTIDE sequencing, GENOMES, NUCLEOTIDES, KARYOTYPES
Abstract

Complex chromosomal rearrangements (CCRs) are rearrangements involving more than two chromosomes or more than two breakpoints. Whole genome sequencing (WGS) allows for outstanding high resolution characterization on the nucleotide level in unique sequences of such rearrangements, but problems remain for mapping breakpoints in repetitive regions of the genome, which are known to be prone to rearrangements. Hence, multiple complementary WGS experiments are sometimes needed to solve the structures of CCRs. We have studied three individuals with CCRs: Case 1 and Case 2 presented with de novo karyotypically balanced, complex interchromosomal rearrangements (46,XX,t(2;8;15)(q35;q24.1;q22) and 46,XY,t(1;10;5)(q32;p12;q31)), and Case 3 presented with a de novo, extremely complex intrachromosomal rearrangement on chromosome 1. Molecular cytogenetic investigation revealed cryptic deletions in the breakpoints of chromosome 2 and 8 in Case 1, and on chromosome 10 in Case 2, explaining their clinical symptoms. In Case 3, 26 breakpoints were identified using WGS, disrupting five known disease genes. All rearrangements were subsequently analyzed using optical maps, linked-read WGS, and short-read WGS. In conclusion, we present a case series of three unique de novo CCRs where we by combining the results from the different technologies fully solved the structure of each rearrangement. The power in combining short-read WGS with long-molecule sequencing or optical mapping in these unique de novo CCRs in a clinical setting is demonstrated. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
View/download PDF

5. AMYCNE: Confident copy number assessment using whole genome sequencing data.

Author

Eisfeldt, Jesper, Nilsson, Daniel, Andersson-Assarsson, Johanna C., and Lindstrand, Anna

Subjects
DNA copy number variations, NUCLEOTIDE sequencing, SEX chromosomes, COMPUTER simulation, COHORT analysis
Abstract

Copy number variations (CNVs) within the human genome have been linked to a diversity of inherited diseases and phenotypic traits. The currently used methodology to measure copy numbers has limited resolution and/or precision, especially for regions with more than 4 copies. Whole genome sequencing (WGS) offers an alternative data source to allow for the detection and characterization of the copy number across different genomic regions in a single experiment. A plethora of tools have been developed to utilize WGS data for CNV detection. None of these tools are designed specifically to accurately estimate copy numbers of complex regions in a small cohort or clinical setting. Herein, we present AMYCNE (automatic modeling functionality for copy number estimation), a CNV analysis tool using WGS data. AMYCNE is multifunctional and performs copy number estimation of complex regions, annotation of VCF files, and CNV detection on individual samples. The performance of AMYCNE was evaluated using AMY1A ddPCR measurements from 86 unrelated individuals. In addition, we validated the accuracy of AMYCNE copy number predictions on two additional genes (FCGR3A and FCGR3B) using datasets available through the 1000 genomes consortium. Finally, we simulated levels of mosaic loss and gain of chromosome X and used this dataset for benchmarking AMYCNE. The results show a high concordance between AMYCNE and ddPCR, validating the use of AMYCNE to measure tandem AMY1 repeats with high accuracy. This opens up new possibilities for the use of WGS for accurate copy number determination of other complex regions in the genome in small cohorts or single individuals. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

6. High-resolution detection of chromosomal rearrangements in leukemias through mate pair whole genome sequencing.

Author

Tran, Anh Nhi, Taylan, Fulya, Zachariadis, Vasilios, Ivanov Öfverholm, Ingegerd, Lindstrand, Anna, Vezzi, Francesco, Lötstedt, Britta, Nordenskjöld, Magnus, Nordgren, Ann, Nilsson, Daniel, and Barbany, Gisela

Subjects
LEUKEMIA, CHROMOSOMAL rearrangement, CYTOGENETICS, NUCLEOTIDE sequencing, KARYOTYPES, PROGNOSIS
Abstract

The detection of recurrent somatic chromosomal rearrangements is standard of care for most leukemia types. Even though karyotype analysis—a low-resolution genome-wide chromosome analysis—is still the gold standard, it often needs to be complemented with other methods to increase resolution. To evaluate the feasibility and applicability of mate pair whole genome sequencing (MP-WGS) to detect structural chromosomal rearrangements in the diagnostic setting, we sequenced ten bone marrow samples from leukemia patients with recurrent rearrangements. Samples were selected based on cytogenetic and FISH results at leukemia diagnosis to include common rearrangements of prognostic relevance. Using MP-WGS and in-house bioinformatic analysis all sought rearrangements were successfully detected. In addition, unexpected complexity or additional, previously undetected rearrangements was unraveled in three samples. Finally, the MP-WGS analysis pinpointed the location of chromosome junctions at high resolution and we were able to identify the exact exons involved in the resulting fusion genes in all samples and the specific junction at the nucleotide level in half of the samples. The results show that our approach combines the screening character from karyotype analysis with the specificity and resolution of cytogenetic and molecular methods. As a result of the straightforward analysis and high-resolution detection of clinically relevant rearrangements, we conclude that MP-WGS is a feasible method for routine leukemia diagnostics of structural chromosomal rearrangements. [ABSTRACT FROM AUTHOR]

Published
2018
Full Text
View/download PDF

7. Further molecular and clinical delineation of co-locating 17p13.3 microdeletions and microduplications that show distinctive phenotypes.

Author

Bruno, Damien L., Anderlid, Britt-Marie, Lindstrand, Anna, van Ravenswaaij-Arts, Conny, Ganesamoorthy, Devika, Lundin, Johanna, Martin, Christa Lese, Douglas, Jessica, Nowak, Catherine, Adam, Margaret P., Kooy, R. Frank, der Aa, Nathalie Van, Reyniers, Edwin, Vandeweyer, Geert, Stolte-Dijkstra, Irene, Dijkhuizen, Trijnie, Yeung, Alison, Delatycki, Martin, Borgström, Birgit, and Thelin, Lena

Subjects
CHROMOSOMES, NUCLEOTIDE sequence, GENOMICS, HUMAN abnormalities, GENETICS, GENES, AUTISM
Abstract

Background Chromosome 17p13.3 contains extensive repetitive sequences and is a recognised region of genomic instability. Haploinsufficiency of PAFAH1B1 (encoding LIS1) causes either isolated lissencephaly sequence or Miller·Dieker syndrome, depending on the size of the deletion. More recently, both microdeletions and microduplications mapping to the Miller·Dieker syndrome telomeric critical region have been identified and associated with distinct but overlapping phenotypes. Methods Genome-wide microarray screening was performed on 7678 patients referred with unexplained learning difficulties and/or autism, with or without other congenital abnormalities. Eight and five unrelated individuals, respectively, were identified with microdeletions and microduplications in 17p13.3. Results Comparisons with six previously reported microdeletion cases identified a 258 kb critical region, encompassing six genes including CRK (encoding Crk) and YWHAE (encoding 14-3-3e). Clinical features included growth retardation, facial dysmorphism and developmental delay. Notably, one individual with only subtle facial features and an interstitial deletion involving CRK but not YWHAE suggested that a genomic region spanning 109 kb, encompassing two genes (TUSC5 and YWHAE), is responsible for the main facial dysmorphism phenotype. Only the microduplication phenotype included autism. The microduplication minimal region of overlap for the new and previously reported cases spans 72 kb encompassing a single gene, YWHAE. These genomic rearrangements were not associated with low-copy repeats and are probably due to diverse molecular mechanisms. Conclusions The authors further characterise the 17p13.3 microdeletion and microduplication phenotypic spectrum and describe a smaller critical genomic region allowing identification of candidate genes for the distinctive facial dysmorphism (microdeletions) and autism (microduplications) manifestations. [ABSTRACT FROM AUTHOR]

Published
2010
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results