Dataset Information

14q12 and severe Rett-like phenotypes: new clinical insights and physical mapping of FOXG1-regulatory elements.

ABSTRACT: The Forkhead box G1 (FOXG1) gene has been implicated in severe Rett-like phenotypes. It encodes the Forkhead box protein G1, a winged-helix transcriptional repressor critical for forebrain development. Recently, the core FOXG1 syndrome was defined as postnatal microcephaly, severe mental retardation, absent language, dyskinesia, and dysgenesis of the corpus callosum. We present seven additional patients with a severe Rett-like neurodevelopment disorder associated with de novo FOXG1 point mutations (two cases) or 14q12 deletions (five cases). We expand the mutational spectrum in patients with FOXG1-related encephalopathies and precise the core FOXG1 syndrome phenotype. Dysgenesis of the corpus callosum and dyskinesia are not always present in FOXG1-mutated patients. We believe that the FOXG1 gene should be considered in severely mentally retarded patients (no speech-language) with severe acquired microcephaly (-4 to-6 SD) and few clinical features suggestive of Rett syndrome. Interestingly enough, three 14q12 deletions that do not include the FOXG1 gene are associated with phenotypes very reminiscent to that of FOXG1-mutation-positive patients. We physically mapped a putative long-range FOXG1-regulatory element in a 0.43 Mb DNA segment encompassing the PRKD1 locus. In fibroblast cells, a cis-acting regulatory sequence located more than 0.6 Mb away from FOXG1 acts as a silencer at the transcriptional level. These data are important for clinicians and for molecular biologists involved in the management of patients with severe encephalopathies compatible with a FOXG1-related phenotype.

SUBMITTER: Allou L

PROVIDER: S-EPMC3499785 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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14q12 and severe Rett-like phenotypes: new clinical insights and physical mapping of FOXG1-regulatory elements.

Allou Lila L Lambert Laetitia L Amsallem Daniel D Bieth Eric E Edery Patrick P Destrée Anne A Rivier François F Amor David D Thompson Elizabeth E Nicholl Julian J Harbord Michael M Nemos Christophe C Saunier Aline A Moustaïne Aissa A Vigouroux Adeline A Jonveaux Philippe P Philippe Christophe C

European journal of human genetics : EJHG 20120627 12

The Forkhead box G1 (FOXG1) gene has been implicated in severe Rett-like phenotypes. It encodes the Forkhead box protein G1, a winged-helix transcriptional repressor critical for forebrain development. Recently, the core FOXG1 syndrome was defined as postnatal microcephaly, severe mental retardation, absent language, dyskinesia, and dysgenesis of the corpus callosum. We present seven additional patients with a severe Rett-like neurodevelopment disorder associated with de novo FOXG1 point mutatio ...[more]

PMID: 22739344

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Project description:BackgroundThe data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).ResultsWe develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.ConclusionsOur exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Dataset Information

14q12 and severe Rett-like phenotypes: new clinical insights and physical mapping of FOXG1-regulatory elements.

Publications

14q12 and severe Rett-like phenotypes: new clinical insights and physical mapping of FOXG1-regulatory elements.

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