The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes

Method for Expanding Search Space With Hybrid Operations in DynamicNAS

Recently, a novel neural architecture search method, which is referred to as DynamicNAS (Dynamic Neural Architecture Search) in this paper, has shown great potential. Not only can various sizes of models be trained with a single training session through DynamicNAS, but the subnets trained by DynamicNAS show improved performance compared to the subnets trained by conventional methods. Although DynamicNAS has many strengths compared to conventional NAS, it has the drawback that different types of operations cannot be used simultaneously within a layer as a search space. In this paper, we present a method that allows DynamicNAS to use different types of operations in a layer as a search space, without undermining the benefits of DynamicNAS, such as one-time training and superior subnet performance. Our experiments show that common operation mixing methods, such as convex combination and set sampling, are inadequate for the problem, although they have a structure that is similar to the proposed method. The proposed method finds, from a supernet of hybrid operations, a superior architecture that cannot be found from a single-operation supernet

Ensemble-Based Out-of-Distribution Detection

To design an efficient deep learning model that can be used in the real-world, it is important to detect out-of-distribution (OOD) data well. Various studies have been conducted to solve the OOD problem. The current state-of-the-art approach uses a confidence score based on the Mahalanobis distance in a feature space. Although it outperformed the previous approaches, the results were sensitive to the quality of the trained model and the dataset complexity. Herein, we propose a novel OOD detection method that can train more efficient feature space for OOD detection. The proposed method uses an ensemble of the features trained using the softmax-based classifier and the network based on distance metric learning (DML). Through the complementary interaction of these two networks, the trained feature space has a more clumped distribution and can fit well on the Gaussian distribution by class. Therefore, OOD data can be efficiently detected by setting a threshold in the trained feature space. To evaluate the proposed method, we applied our method to various combinations of image datasets. The results show that the overall performance of the proposed approach is superior to those of other methods, including the state-of-the-art approach, on any combination of datasets

Neuronal Properties, In Vivo Effects, and Pathology of a Huntington's Disease Patient-Derived Induced Pluripotent Stem Cells

Induced pluripotent stem cells (iPSCs) generated from somatic cells of patients can be used to model different human diseases. They may also serve as sources of transplantable cells that can be used in novel cell therapies. Here, we analyzed neuronal properties of an iPSC line derived from a patient with a juvenile form of Huntington's disease (HD) carrying 72 CAG repeats (HD-iPSC). Although its initial neural inducing activity was lower than that of human embryonic stem cells, we found that HD-iPSC can give rise to GABAergic striatal neurons, the neuronal cell type that is most susceptible to degeneration in HD. We then transplanted HD-iPSC-derived neural precursors into a rat model of HD with a unilateral excitotoxic striatal lesion and observed a significant behavioral recovery in the grafted rats. Interestingly, during our in vitro culture and when the grafts were examined at 12 weeks after transplantation, no aggregate formation was detected. However, when the culture was treated with a proteasome inhibitor (MG132) or when the cells engrafted into neonatal brains were analyzed at 33 weeks, there were clear signs of HD pathology. Taken together, these results indicate that, although HD-iPSC carrying 72 CAG repeats can form GABAergic neurons and give rise to functional effects in vivo, without showing an overt HD phenotype, it is highly susceptible to proteasome inhibition and develops HD pathology at later stages of transplantation. These unique features of HD-iPSC will serve as useful tools to study HD pathology and develop novel therapeutics. Stem Cells 2012; 30: 2054206

Pre-structured motifs in the natively unstructured preS1 surface antigen of hepatitis B virus

The preS1 surface antigen of hepatitis B virus (HBV) is known to play an important role in the initial attachment of HBV to hepatocytes. We have characterized structural features of the full-length preS1 using heteronuclear NMR methods and discovered that this 119-residue protein is inherently unstructured without a unique tertiary structure under a nondenaturing condition. Yet, combination of various NMR parameters shows that the preS1 contains “pre-structured” domains broadly covering its functional domains. The most prominent domain is formed by residues 27–45 and overlaps with the putative hepatocyte-binding domain (HBD) encompassing residues 21–47, within which two well-defined pre-structured motifs, formed by Pro32–Ala36 and Pro41–Phe45 are found. Additional, somewhat less prominent, pre-structured motifs are also formed by residues 11–18, 22–25, 37–40, and 46–50. Overall results suggest that the preS1 is a natively unstructured protein (NUP) whose N-terminal 50 residues, populated with multiple pre-structured motifs, contribute critically to hepatocyte binding

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes