The phenotype of floating-harbor syndrome:clinical characterization of 52 individuals with mutations in exon 34 of SRCAP

Background\ud Floating-Harbor syndrome (FHS) is a rare condition characterized by short stature, delays in expressive language, and a distinctive facial appearance. Recently, heterozygous truncating mutations in SRCAP were determined to be disease-causing. With the availability of a DNA based confirmatory test, we set forth to define the clinical features of this syndrome.\ud \ud Methods and results\ud Clinical information on fifty-two individuals with SRCAP mutations was collected using standardized questionnaires. Twenty-four males and twenty-eight females were studied with ages ranging from 2 to 52 years. The facial phenotype and expressive language impairments were defining features within the group. Height measurements were typically between minus two and minus four standard deviations, with occipitofrontal circumferences usually within the average range. Thirty-three of the subjects (63%) had at least one major anomaly requiring medical intervention. We did not observe any specific phenotype-genotype correlations.\ud \ud Conclusions\ud This large cohort of individuals with molecularly confirmed FHS has allowed us to better delineate the clinical features of this rare but classic genetic syndrome, thereby facilitating the development of management protocols.The authors would like to thank the families for their cooperation and permission to publish these findings. SdM would like to thank Barto Otten. Funding was provided by the Government of Canada through Genome Canada, the Canadian Institutes of Health Research (CIHR) and the Ontario Genomics Institute (OGI-049), by Genome Québec and Genome British Columbia, and the Manton Center for Orphan Disease Research at Children’s Hospital Boston. KMB is supported by a Clinical Investigatorship Award from the CIHR Institute of Genetics. AD is supported by NIH grant K23HD073351. BBAdV and HGB were financially supported by the AnEUploidy project (LSHG-CT-2006-37627). This work was selected for study by the FORGE Canada Steering Committee, which consists of K. Boycott (University of Ottawa), J. Friedman (University of British Columbia), J. Michaud (University of Montreal), F. Bernier (University of Calgary), M. Brudno (University of Toronto), B. Fernandez (Memorial University), B. Knoppers (McGill University), M. Samuels (Université de Montréal), and S. Scherer (University of Toronto). We thank the Galliera Genetic Bank - “Telethon Genetic Biobank Network” supported by Italian Telethon grants (project no. GTB07001) for providing us with specimens

The phenotype of Floating-Harbor syndrome: Clinical characterization of 52 individuals with mutations in exon 34 of SRCAP

Background: Floating-Harbor syndrome (FHS) is a rare condition characterized by short stature, delays in expressive language, and a distinctive facial appearance. Recently, heterozygous truncating mutations in SRCAP were determined to be disease-causing. With the availability of a DNA based confirmatory test, we set forth to define the clinical features of this syndrome. Methods and results. Clinical information on fifty-two individuals with SRCAP mutations was collected using standardized questionnaires. Twenty-four males and twenty-eight females were studied with ages ranging from

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

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