A Regression-based Approach to Robust Estimation and Inference for Genetic Covariance

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits, and some variants are shown to be associated with multiple complex traits. Genetic covariance between two traits is defined as the underlying covariance of genetic effects and can be used to measure the shared genetic architecture. The data used to estimate such a genetic covariance can be from the same group or different groups of individuals, and the traits can be of different types or collected based on different study designs. This paper proposes a unified regression-based approach to robust estimation and inference for genetic covariance of general traits that may be associated with genetic variants nonlinearly. The asymptotic properties of the proposed estimator are provided and are shown to be robust under certain model mis-specification. Our method under linear working models provides a robust inference for the narrow-sense genetic covariance, even when both linear models are mis-specified. Numerical experiments are performed to support the theoretical results. Our method is applied to an outbred mice GWAS data set to study the overlapping genetic effects between the behavioral and physiological phenotypes. The real data results reveal interesting genetic covariance among different mice developmental traits.</p

Large Covariance Estimation for Compositional Data Via Composition-Adjusted Thresholding

<p>High-dimensional compositional data arise naturally in many applications such as metagenomic data analysis. The observed data lie in a high-dimensional simplex, and conventional statistical methods often fail to produce sensible results due to the unit-sum constraint. In this article, we address the problem of covariance estimation for high-dimensional compositional data and introduce a composition-adjusted thresholding (COAT) method under the assumption that the basis covariance matrix is sparse. Our method is based on a decomposition relating the compositional covariance to the basis covariance, which is approximately identifiable as the dimensionality tends to infinity. The resulting procedure can be viewed as thresholding the sample centered log-ratio covariance matrix and hence is scalable for large covariance matrices. We rigorously characterize the identifiability of the covariance parameters, derive rates of convergence under the spectral norm, and provide theoretical guarantees on support recovery. Simulation studies demonstrate that the COAT estimator outperforms some existing optimization-based estimators. We apply the proposed method to the analysis of a microbiome dataset to understand the dependence structure among bacterial taxa in the human gut.</p

Efektivitas pembelajaran eksperimen terhadap hasil belajar, pemahaman konsep, dan keaktifan siswa kelas X SMA Negeri 2 Yogyakarta pada pokok bahasan gerak harmonik sederhana tahun ajaran 2016-2017

<p>Diffuse, cytoplasmic GTTR fluorescence was detected in saccular and utricular hair cells at 0.5 hours and significantly increased in intensity over time to peak at 3 hours after systemic injection of GTTR. At 4 hours, diffuse cytoplasmic fluorescence was significantly attenuated compared to the 3 hour time point (* p<0.05, ** p<0.01, ***p<0.001; mean ± s.d.; n = 5).</p

Intensity of GTTR fluorescence in the LSC cristae over time.

<p>(A) The intensity of diffuse GTTR fluorescence in dark cells (DC) at 0.5 hours significantly increased over time to a peak value at 3 hours before declining. Diffuse GTTR fluorescence in transitional cells (TC), hair cells (HC), and supporting cells (SC) also increased in a similar manner, peaking at 3 hours before declining at 4 hours. (B) Comparison of diffuse cytoplasmic GTTR fluorescence in the different cell types of the LSC using one way ANOVA with a post hoc test revealed significantly increased intensity of diffuse GTTR fluorescence in transitional cells compared to dark cells, hair cells, and supporting cells (p<0.01) at 0.5 hour. At 1 hour, diffuse GTTR fluorescence in transitional cells continued to be significantly higher than in hair cells and supporting cells. At later time-points (2, 3, and 4 hours), no significant difference was found in the diffuse GTTR fluorescence of dark cells, transitional cells, hair cells, and supporting cells (p>0.05). (C) The intensity of punctate GTTR fluorescence in dark cells, transitional cells, and supporting cells at 0.5 hours significantly increased over time to a peak at 3 hours, and did not decline significantly at 4 hours. (D) Comparison of GTTR puncta intensity in the different cell types of the LSC using one way ANOVA with a post hoc test revealed significantly increased intensity of GTTR puncta in transitional cells compared to supporting cells (p<0.05) at 0.5 hours. At 1 hour, GTTR puncta in transitional cells and dark cells were significantly more intense than in supporting cells (p<0.01). At 2 hours, only GTTR puncta in transitional cells were significantly more intense compared to supporting cells (p<0.01). At 3 and 4 hours, puncta GTTR fluorescence in dark cells, transitional cells, and supporting cells was not significantly different (p>0.05). (For A-D: * p<0.05, ** p<0.01, ***p<0.001; mean ± s.d.; n = 5).</p

GTTR fluorescence in the lateral semicircular canal (LSC) peaked 3 hours after a single systemic injection of GTTR.

<p>At 0.5 hours, intense fluorescent puncta were readily seen in dark cells (A1) and transitional cells (A2), with less intense puncta seen in sensory epithelia (A3). Low intensity diffuse GTTR fluorescence was also detected in dark cells (A1) and transitional cells (A2), with weaker fluorescence in supporting cells and sensory hair cells in the sensory epithelia of the LSC crista (A3). At 1 hour, dark cells (B1), transitional cells (B2), and sensory epithelia (B3) had increased numbers of puncta and higher fluorescence intensity compared to at 0.5 hours (A1-A3). Increased intensity of diffuse cytosolic GTTR fluorescence was also observed in dark cells (B2), transitional cells (B2), and sensory epithelia (B3). Two hours after GTTR injection, increased cytosolic GTTR fluorescence was apparent in dark cells (C2), but less so in transitional cells (C2) and sensory epithelia (C3). Also, increased numbers of fluorescent puncta were found in dark cells (C1), transitional cells (C2), and sensory epithelia (C3), compared to earlier time points (A1-B3). Fluorescent intensity peaked at 3 hours, and declined by 4 hours (E1-E3), in all three regions. Mice injected with hydrolyzed Texas Red for 2 hours showed negligible fluorescence in all three vestibular regions (F1-F3). Scale bar in A3 = 20 <i>μ</i>m.</p

Vestibular hair cells from individual end-organs exhibit equivalent fluorescence after systemic injection of GTTR.

<p>No significant difference was observed in the cytoplasmic GTTR intensity of hair cells from the three cristae and two maculae at any time point after systemic injection of GTTR (one-way ANOVA, p>0.05; mean ± s.d.; n = 5). LSC: lateral semicircular canal; PSC: posterior semicircular canal; SSC: superior semicircular canal.</p

DataSheet1_Constructing a signature based on the SIRT family to help the prognosis and treatment sensitivity in glioma patients.xlsx

Enzymes of the silent information regulator (SIRT) family exert crucial roles in basic cellular physiological processes including apoptosis, metabolism, ageing, and cell cycle progression. They critically contribute to promoting or inhibiting cancers such as glioma. In the present study, a new gene signature of this family was identified for use in risk assessment and stratification of glioma patients. To this end, the transcriptome and relevant clinical records of patients diagnosed with glioma were obtained from the Cancer Genomic Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LASSO regression and multivariate Cox analyses were used to establish the signature. Using Kaplan–Meier analyses, overall survival (OS) was assessed and compared between a training and an external test datasets which showed lower OS in patients with high risk of glioma compared to those with low risk. Further, ROC curve analyses indicated that the SIRT-based signature had the desired accuracy and universality for evaluating the prognosis of glioma patients. Using univariate and multivariate Cox regression analyses, the SIRT-based signature was confirmed as an independent prognostic factor applicable to subjects in the TCGA and CGGA databases. We also developed an OS nomogram including gender, age, risk score, pathological grade, and IDH status for clinical decision-making purposes. ssGSEA analysis showed a higher score for various immune subgroups (e.g., CD8+ T cells, DC, and TIL) in samples from high-risk patients, compared to those of low-risk ones. qPCR and western blotting confirmed the dysregulated expression of SIRTs in gliomas. Taken together, we developed a new signature on the basis of five SIRT family genes, which can help accurately predict OS of glioma patients. In addition, the findings of the present study suggest that this characteristic is associated with differences in immune status and infiltration levels of various immune cells in the tumor microenvironment.</p

Sparse Simultaneous Signal Detection for Identifying Genetically Controlled Disease Genes

<p>Genome-wide association studies (GWAS) and differential expression analyses have had limited success in finding genes that cause complex diseases such as heart failure (HF), a leading cause of death in the United States. This article proposes a new statistical approach that integrates GWAS and expression quantitative trait loci (eQTL) data to identify important HF genes. For such genes, genetic variations that perturb its expression are also likely to influence disease risk. The proposed method thus tests for the presence of simultaneous signals: SNPs that are associated with the gene’s expression as well as with disease. An analytic expression for the <i>p</i>-value is obtained, and the method is shown to be asymptotically adaptively optimal under certain conditions. It also allows the GWAS and eQTL data to be collected from different groups of subjects, enabling investigators to integrate public resources with their own data. Simulation experiments show that it can be more powerful than standard approaches and also robust to linkage disequilibrium between variants. The method is applied to an extensive analysis of HF genomics and identifies several genes with biological evidence for being functionally relevant in the etiology of HF. It is implemented in the R package ssa. Supplementary materials for this article are available online.</p

Heterologous expression of SGLT2 in KDT3 cells increased cellular uptake of GTTR.

<p>(A–C) KDT3-SGLT2 cells with positive SGLT2 immunofluorescence displayed robust GTTR uptake (B, C). (D–F) Empty vector control clones (KPT2-pBabe) showed negligible SGLT2 immunofluorescence (D) and weak, uniform levels of GTTR fluorescence (E, F) compared to (B, C). (H, I) GTTR fluorescence in KDT3-SGLT2 cells in the presence of phlorizin (100 µg/ml) was visibly less intense than in KDT3-SGLT2 cells without phlorizin treatment (B, C). (K, L) GTTR fluorescence in phlorizin-treated KDT3-pBabe cells showed weak levels of GTTR fluorescence as untreated in KDT3-pBabe cells (E, F). Scale bar = 20 µm. (M) Fluorescence intensities of GTTR in KDT3-SGLT2 or KDT3-pBabe cells in the presence or absence of phlorizin (100 µg/ml; **<i>p</i><0.01).</p

Uptake of the fluorescent glucose analog 2-NBDG is mediated by SGLT2 in KPT2 cells.

<p>KPT2 cells (A) had robust SGLT2 immunolabeling compared to KDT3 cells (B). Increasing doses of (C–F) D-glucose (molar ratios of 1∶0, 1∶1, 1∶50 or 1∶1000 [2-NBDG/D-glucose]), or (G–J) phlorizin (molar ratios of 1∶0, 1∶1, 1∶10 or 1∶50 [2-NBDG/phlorizin]) dose-dependently decreased 2-NBDG fluorescence in KPT2. Scale bar = 20 µm. (K, L). The fluorescence intensity of 2-NBDG in KPT2 cells was significantly decreased with increasing doses of D-glucose (K) or phlorizin (L; **<i>p</i><0.01).</p