Search

Most polygenic risk score (PRS)models have been based on data from populations of European origins (accounting for the majority of the large genomics datasets, e.g. >78% in the UK Biobank and >85% in the GTEx project). Although several large-scale Asian biobanks were initiated (e.g. Japanese, Korean, Han Chinese biobanks), most other Asian countries have little or near-zero genomics data. To implement PRS models for under-represented populations, we explored transfer learning approaches, assuming that information from existing large datasets can compensate for the small sample size that can be feasibly obtained in developing countries, like Vietnam. Here, we benchmark 13 common PRS methods in meta-population strategy (combining individual genotype data from multiple populations) and multi-population strategy (combining summary statistics from multiple populations). Our results highlight the complementarity of different populations and the choice of methods should depend on the target population. Based on these results, we discussed a set of guidelines to help users select the best method for their datasets. We developed a robust and comprehensive software to allow for benchmarking comparisons between methods and proposed a computational framework for improving PRS performance in a dataset with a small sample size. This work is expected to inform the development of genomics applications in under-represented populations. PRSUP framework is available at: https://github.com/BiomedicalMachineLearning/VGP [ABSTRACT FROM AUTHOR]

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole-genome sequencing databases. Our results show that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited. |$\textrm{LmTag}$| is available at: https://github.com/datngu/LmTag. [ABSTRACT FROM AUTHOR]

We have characterized a recently rediscovered chemosensory structure at the rear of the mandibular mucosa in the mouse oral cavity originally reported in the 1980s. This consists of unorganized taste buds, not contained within troughs, associated with the ducts of an underlying minor salivary gland. Using whole-mount preparations of transgenic mice expressing green fluorescent protein under the promoter of taste-signaling-specific genes, we determined that the structure contains taste bud clusters and salivary gland orifices at the rear of each mandible, distal to the last molar and anterior to the ascending ramus. Immunohistochemical analysis shows in the retromolar taste buds expression of the taste receptors Tas2R131 and T1R3 and taste cascade molecules TrpM5, PLCβ2, and GNAT3, consistent with type II taste cells, and expression of GAD1, consistent with type III taste cells. Furthermore, the neuronal marker, calcitonin gene-related peptide, in retromolar mucosa tissue wrapping around TrpM5+ taste buds was observed. RT–PCR showed that retromolar taste buds express all 3 mouse tas1r genes, 28 of the 35 tas2r genes, and taste transduction signaling genes gnat3, plcb2 , and trpm5 , making the retromolar taste buds similar to other lingual and palate taste buds. Finally, histochemistry demonstrated that the mandibular retromolar secretory gland is a minor salivary gland of mucous type. The mandibular retromolar taste structure may thus play a role in taste sensation and represent a potential novel pharmacological target for taste disorders. [ABSTRACT FROM AUTHOR]

The evolution of fairy tales often involves complex interactions between oral and literary traditions, which can be difficult to tease apart when investigating their origins. Here, we show how computer-assisted stemmatology can be productively applied to this problem, focusing on a long-standing controversy in fairy tale scholarship: did Little Red Riding Hood originate as an oral tale that was adapted by Perrault and the Brothers Grimm, or is the oral tradition in fact derived from literary texts? We address this question by analysing a sample of twenty-four literal and oral versions of the fairy tale Little Red Riding Hood using several methods of phylogenetic analysis, including maximum parsimony and two network- based approaches (NeighbourNet and TRex). While the results of these analyses are more compatible with the oral origins hypothesis than the alternative literary origins hypothesis, their interpretation is problematized by the fact that none of them explicitly model lineal (i.e. ancestor-descendent) relationships among taxa. We therefore present a new likelihood-based method, PhyloDAG, which was specifically developed to model lineal as well as collateral and reticulate relationships. A comparison of different structures derived from PhyloDAG provided a much clearer result than the maximum parsimony, NeighbourNet or TRex analyses, and strongly favoured the hypothesis that literary versions of Little Red Riding Hood were originally based on oral folktales, rather than vice versa. [ABSTRACT FROM AUTHOR]

Optical coherence tomography studies in multiple sclerosis have primarily focused on evaluation of the retinal nerve fibre layer. The aetiology of retinal changes in multiple sclerosis is thought to be secondary to optic nerve demyelination. The objective of this study was to use optical coherence tomography to determine if a subset of patients with multiple sclerosis exhibit primary retinal neuronopathy, in the absence of retrograde degeneration of the retinal nerve fibre layer and to ascertain if such patients may have any distinguishing clinical characteristics. We identified 50 patients with multiple sclerosis with predominantly macular thinning (normal retinal nerve fibre-layer thickness with average macular thickness <5th percentile), a previously undescribed optical coherence tomography defined phenotype in multiple sclerosis, and compared them with 48 patients with multiple sclerosis with normal optical coherence tomography findings, 48 patients with multiple sclerosis with abnormal optical coherence tomography findings (typical for multiple sclerosis) and 86 healthy controls. Utilizing a novel retinal segmentation protocol, we found that those with predominant macular thinning had significant thinning of both the inner and outer nuclear layers, when compared with other patients with multiple sclerosis (P < 0.001 for both), with relative sparing of the ganglion cell layer. Inner and outer nuclear layer thicknesses in patients with non-macular thinning predominant multiple sclerosis were not different from healthy controls. Segmentation analyses thereby demonstrated extensive deeper disruption of retinal architecture in this subtype than may be expected due to retrograde degeneration from either typical clinical or sub-clinical optic neuropathy. Functional corroboration of retinal dysfunction was provided through multi-focal electroretinography in a subset of such patients. These findings support the possibility of primary retinal pathology in a subset of patients with multiple sclerosis. Multiple sclerosis-severity scores were also significantly increased in patients with the macular thinning predominant phenotype, compared with those without this phenotype (n = 96, P = 0.006). We have identified a unique subset of patients with multiple sclerosis in whom there appears to be disproportionate thinning of the inner and outer nuclear layers, which may be occurring as a primary process independent of optic nerve pathology. In vivo analyses of retinal layers in multiple sclerosis have not been previously performed, and structural demonstration of pathology in the deeper retinal layers, such as the outer nuclear layer, has not been previously described in multiple sclerosis. Patients with inner and outer nuclear layer pathology have more rapid disability progression and thus retinal neuronal pathology may be a harbinger of a more aggressive form of multiple sclerosis. [ABSTRACT FROM AUTHOR]

Motivation Spatial transcriptomics (ST) technology is increasingly being applied because it enables the measurement of spatial gene expression in an intact tissue along with imaging morphology of the same tissue. However, current analysis methods for ST data do not use image pixel information, thus missing the quantitative links between gene expression and tissue morphology. Results We developed a user-friendly deep learning software, SpaCell, to integrate millions of pixel intensity values with thousands of gene expression measurements from spatially barcoded spots in a tissue. We show the integration approach outperforms the use of gene-count data alone or imaging data alone to build deep learning models to identify cell types or predict labels of tissue images with high resolution and accuracy. Availability and implementation The SpaCell package is open source under an MIT licence and it is available at https://github.com/BiomedicalMachineLearning/SpaCell. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background Recent developments in single-cell RNA sequencing (scRNA-seq) platforms have vastly increased the number of cells typically assayed in an experiment. Analysis of scRNA-seq data is multidisciplinary in nature, requiring careful consideration of the application of statistical methods with respect to the underlying biology. Few analysis packages exist that are at once robust, are computationally fast, and allow flexible integration with other bioinformatics tools and methods. Findings ascend is an R package comprising tools designed to simplify and streamline the preliminary analysis of scRNA-seq data, while addressing the statistical challenges of scRNA-seq analysis and enabling flexible integration with genomics packages and native R functions, including fast parallel computation and efficient memory management. The package incorporates both novel and established methods to provide a framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and a wide range of visualization functions. Conclusions ascend is designed to work with scRNA-seq data generated by any high-throughput platform and includes functions to convert data objects between software packages. The ascend workflow is simple and interactive, as well as suitable for implementation by a broad range of users, including those with little programming experience. [ABSTRACT FROM AUTHOR]