De novo variants in H3-3A and H3-3B are associated with neurodevelopmental delay, dysmorphic features, and structural brain abnormalities

The histone H3 variant H3.3, encoded by two genes H3-3A and H3-3B, can replace canonical isoforms H3.1 and H3.2. H3.3 is important in chromatin compaction, early embryonic development, and lineage commitment. The role of H3.3 in somatic cancers has been studied extensively, but its association with a congenital disorder has emerged just recently. Here we report eleven de novo missense variants and one de novo stop-loss variant in H3-3A (n = 6) and H3-3B (n = 6) from Baylor Genetics exome cohort (n = 11) and Matchmaker Exchange (n = 1), of which detailed phenotyping was conducted for 10 individuals (H3-3A = 4 and H3-3B = 6) that showed major phenotypes including global developmental delay, short stature, failure to thrive, dysmorphic facial features, structural brain abnormalities, hypotonia, and visual impairment. Three variant constructs (p.R129H, p.M121I, and p.I52N) showed significant decrease in protein expression, while one variant (p.R41C) accumulated at greater levels than wild-type control. One H3.3 variant construct (p.R129H) was found to have stronger interaction with the chaperone death domain-associated protein 6.

Neurodevelopmental Disorder With Microcephaly, Hypotonia, And Variable Brain Anomalies In A Consanguineous Iranian Family Is Associated With A Novel Homozygous Start Loss Variant In The PRUNE1 Gene

Background: Homozygous or compound heterozygous PRUNE1 mutations cause a neurodevelopmental disorder with microcephaly, hypotonia, and variable brain malformations (NMIHBA) (OMIM #617481). The PRUNE1 gene encodes a member of the phosphoesterase (DHH) protein superfamily that is involved in the regulation of cell migration. To date, most of the described mutations in the PRUNE1 gene are clustered in DHH domain. Methods: We subjected 4 members (two affected and two healthy) of a consanguineous Iranian family in the study. The proband underwent whole-exome sequencing and a novel identified variant was confirmed by Sanger sequencing. Co-segregation of the detected variant with the disease in family was confirmed.Results: By whole-exome sequencing, we identified the first start loss variant, NM_021222.3:c.3G>A; p.(Met1?), in the PRUNE1 in two patients of a consanguineous Iranian family with spastic quadriplegic cerebral palsy (CP), hypotonia, developmental regression, and cerebellar atrophy. Sanger sequencing confirmed the segregation of the variant with the disease in the family. Protein structure analysis also revealed that the variant probably leads to the deletion of DHH (Asp-His-His) domain, the active site of the protein, and loss of PRUNE1 function. Conclusion: We identified a novel start loss variant, NM_021222.3:c.3G>A; p.(Met1?) in the PRUNE1 gene in two affected members as a possible cause of NMIHBA in an Iranian family. We believe that the study adds a new pathogenic variant in spectrum of mutations in the PRUNE1 gene as a cause of PRUNE1-related syndrome.

Generative Adversarial Networks in Computer Vision

Generative adversarial networks (GANs) have been extensively studied in the past few years. Arguably their most significant impact has been in the area of computer vision where great advances have been made in challenges such as plausible image generation, image-to-image translation, facial attribute manipulation, and similar domains. Despite the significant successes achieved to date, applying GANs to real-world problems still poses significant challenges, three of which we focus on here. These are as follows: (1) the generation of high quality images, (2) diversity of image generation, and (3) stabilizing training. Focusing on the degree to which popular GAN technologies have made progress against these challenges, we provide a detailed review of the state-of-the-art in GAN-related research in the published scientific literature. We further structure this review through a convenient taxonomy we have adopted based on variations in GAN architectures and loss functions. While several reviews for GANs have been presented to date, none have considered the status of this field based on their progress toward addressing practical challenges relevant to computer vision. Accordingly, we review and critically discuss the most popular architecture-variant, and loss-variant GANs, for tackling these challenges. Our objective is to provide an overview as well as a critical analysis of the status of GAN research in terms of relevant progress toward critical computer vision application requirements. As we do this we also discuss the most compelling applications in computer vision in which GANs have demonstrated considerable success along with some suggestions for future research directions. Codes related to the GAN-variants studied in this work is summarized on https://github.com/sheqi/GAN_Review.

SUN-690 HNF4A Mutation in Siblings with Diazoxide Responsive Congenital Hyperinsulinism

Background: Congenital hyperinsulinism (HI) is the leading cause of severe, persistent hypoglycemia in infants. Transient HI seen at risk neonates due to prenatal stress and some of the congenital HI cases due to mutations in K-ATPase channel are responsive to diazoxide. It is not a common practice to obtain genetic evaluation for diazoxide responsive HI. However, children with dominant inactivating variants in HNF4A gene may present with diazoxide-responsive HI and mimic transient HI in infancy. Objective: To describe two siblings with diazoxide responsive HI with HNF4A mutation associated with maturity onset diabetes of youth type 1 (MODY1). Clinical Case: Case 1, term female with macrosomia and Case 2, preterm male appropriate for gestational age were born to same mother without gestational diabetes and with no perinatal stress. Siblings were non-dysmorphic and both presented with hypoglycemia during first week of life. Diagnosis of HI is confirmed based on inappropriately suppressed β-hydroxybutyrate at the time of hypoglycemia and inappropriate glycemic response to glucagon consistent with increased insulin action. Both siblings responded to diazoxide therapy. Family history was significant for late-onset diabetes in paternal extended family. Case 1 required very low dose diazoxide (2 mg/kg/day) during first year of life to sustain normoglycemia. She came off of diazoxide at 19 months of age. Case 2 is normoglycemic on 5mg/kg/day diazoxide at 4 months of age. Genetic evaluation through whole exome sequencing pursued upon diagnosis of Case 2 revealed paternally inherited heterozygous pathogenic start loss variant in HNF4A gene (c.3G>T) in both siblings. Father was completely asymptomatic without any history of hypo- or hyperglycemia. Conclusion: HNF4A gene encodes hepatocyte nuclear factor-4-alpha that regulates hepatic gluconeogenesis and lipid metabolism. Dominant inactivating variants in HNF4A gene associated with familial HI, are typically associated with increased size for gestational age, mild diazoxide-responsive hypoglycemia (which may be transient) and monogenic diabetes during adolescence. HNF4A mutations were described as one of the most common genetic cause of diazoxide-responsive congenital hyperinsulinism and are associated with MODY1. It is important to consider genetic evaluation in diazoxide responsive HI cases. Identifying children with HNF4A variant early on will impact their long-term follow-up leading to earlier diagnosis and treatment of MODY-1 and potentially improve long-term outcomes.

Expert Specification of the ACMG/AMP Variant Interpretation Guidelines for Genetic Hearing Loss

ABSTRACTDue to the high genetic heterogeneity of hearing loss, current clinical testing includes sequencing large numbers of genes, which often yields a significant number of novel variants. Therefore, the standardization of variant interpretation is crucial to provide consistent and accurate diagnoses. The Hearing Loss Variant Curation Expert Panel was created within the Clinical Genome Resource to provide expert guidance for standardized genomic interpretation in the context of hearing loss. As one of its major tasks, our Expert Panel has adapted the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) standards and guidelines for the interpretation of sequence variants in hearing loss genes. Here, we provide a comprehensive illustration of the newly specified ACMG/AMP hearing loss rules. Three rules remained unchanged, four rules were removed, and the remaining twenty-one rules were specified. Of the specified rules, four had general recommendations, seven were gene/disease considerations, seven had strength-level specifications, and three rules had both gene/disease and strength-level specifications. These rules were further validated and refined using a pilot set of 51 variants assessed by curators. These hearing loss-specific ACMG/AMP rules will help standardize variant interpretation, ultimately leading to better care for individuals with hearing loss.GRANT NUMBERSResearch reported in this publication was supported by the National Human Genome Research Institute (NHGRI) under award number U41HG006834.

Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization

Regression aims at estimating the conditional mean of output given input. However, regression is not informative enough if the conditional density is multimodal, heteroskedastic, and asymmetric. In such a case, estimating the conditional density itself is preferable, but conditional density estimation (CDE) is challenging in high-dimensional space. A naive approach to coping with high dimensionality is to first perform dimensionality reduction (DR) and then execute CDE. However, a two-step process does not perform well in practice because the error incurred in the first DR step can be magnified in the second CDE step. In this letter, we propose a novel single-shot procedure that performs CDE and DR simultaneously in an integrated way. Our key idea is to formulate DR as the problem of minimizing a squared-loss variant of conditional entropy, and this is solved using CDE. Thus, an additional CDE step is not needed after DR. We demonstrate the usefulness of the proposed method through extensive experiments on various data sets, including humanoid robot transition and computer art.

Dependence-Maximization Clustering with Least-Squares Mutual Information

Recently, statistical dependence measures such as mutual information and kernelized covariance have been successfully applied to clustering. In this paper, we follow this line of research and propose a novel dependence-maximization clustering method based on least-squares mutual information, which is an estimator of a squared-loss variant of mutual information. A notable advantage of the proposed method over existing approaches is that hyperparameters such as kernel parameters and regularization parameters can be objectively optimized based on cross-validation. Thus, subjective manual-tuning of hyperparameters is not necessary in the proposed method, which is a highly useful property in unsupervised clustering scenarios. Through experiments, we illustrate the usefulness of the proposed approach.