Genome-wide analyses of common and rare genetic variations have documented the heritability of major psychiatric disorders, established their highly polygenic genetic architecture, and identified ...hundreds of contributing variants. In recent years, these studies have illuminated another key feature of the genetic basis of psychiatric disorders: the important role and pervasive nature of pleiotropy. It is now clear that a substantial fraction of genetic influences on psychopathology transcend clinical diagnostic boundaries. In this review, we summarize evidence in psychiatry for pleiotropy at multiple levels of analysis: from overall genome-wide correlation to biological pathways and down to the level of individual loci. We examine underlying mechanisms of observed pleiotropy, including genetic effects on neurodevelopment, diverse actions of regulatory elements, mediated effects, and spurious associations of genomic variation with multiple phenotypes. We conclude with an exploration of the implications of pleiotropy for understanding the genetic basis of psychiatric disorders, informing nosology, and advancing the aims of precision psychiatry and genomic medicine.
Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases ...in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (r
= 0.75, P = 6.84 × 10
). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases.
Here we present INRICH (INterval enRICHment analysis), a pathway-based genome-wide association analysis tool that tests for enriched association signals of predefined gene-sets across independent ...genomic intervals. INRICH has wide applicability, fast running time and, most importantly, robustness to potential genomic biases and confounding factors. Such factors, including varying gene size and single-nucleotide polymorphism density, linkage disequilibrium within and between genes and overlapping genes with similar annotations, are often not accounted for by existing gene-set enrichment methods. By using a genomic permutation procedure, we generate experiment-wide empirical significance values, corrected for the total number of sets tested, implicitly taking overlap of sets into account. By simulation we confirm a properly controlled type I error rate and reasonable power of INRICH under diverse parameter settings. As a proof of principle, we describe the application of INRICH on the NHGRI GWAS catalog.
A standalone C++ program, user manual and datasets can be freely downloaded from: http://atgu.mgh.harvard.edu/inrich/.
Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that ...pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.
We propose a communication-efficient transfer learning approach (COMMUTE) that effectively incorporates multi-site healthcare data for training a risk prediction model in a target population of ...interest, accounting for challenges including population heterogeneity and data sharing constraints across sites.
We first train population-specific source models locally within each site. Using data from a given target population, COMMUTE learns a calibration term for each source model, which adjusts for potential data heterogeneity through flexible distance-based regularizations. In a centralized setting where multi-site data can be directly pooled, all data are combined to train the target model after calibration. When individual-level data are not shareable in some sites, COMMUTE requests only the locally trained models from these sites, with which, COMMUTE generates heterogeneity-adjusted synthetic data for training the target model. We evaluate COMMUTE via extensive simulation studies and an application to multi-site data from the electronic Medical Records and Genomics (eMERGE) Network to predict extreme obesity.
Simulation studies show that COMMUTE outperforms methods without adjusting for population heterogeneity and methods trained in a single population over a broad spectrum of settings. Using eMERGE data, COMMUTE achieves an area under the receiver operating characteristic curve (AUC) around 0.80, which outperforms other benchmark methods with AUC ranging from 0.51 to 0.70.
COMMUTE improves the risk prediction in a target population with limited samples and safeguards against negative transfer when some source populations are highly different from the target. In a federated setting, it is highly communication efficient as it only requires each site to share model parameter estimates once, and no iterative communication or higher-order terms are needed.
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Over a decade of genome-wide association, studies have made great strides toward the detection of genes and genetic mechanisms underlying complex traits. However, the majority of associated loci ...reside in non-coding regions that are functionally uncharacterized in general. Now, the availability of large-scale tissue and cell type-specific transcriptome and epigenome data enables us to elucidate how non-coding genetic variants can affect gene expressions and are associated with phenotypic changes. Here, we provide an overview of this emerging field in human genomics, summarizing available data resources and state-of-the-art analytic methods to facilitate in-silico prioritization of non-coding regulatory mutations. We also highlight the limitations of current approaches and discuss the direction of much-needed future research.
We have demonstrated that the FeCl2·4H2O and methanesulfonic acid systems show high reaction efficiency for the indirect hydration of various alkynes in DCE. The reaction proceeds under mild ...conditions to produce various ketones from alkynes. A mechanistic study of the reaction intermediates showed that the alkyne was readily converted into vinyl sulfonate corresponding to the addition of MsOH to the C–C triple bonds, which in turn was transformed to the ketone in the presence of MsOH.
Background/AimsRetinal capillary non-perfusion (NP) and neovascularisation (NV) are two of the most important angiographic changes in diabetic retinopathy (DR). This study investigated the ...feasibility of using deep learning (DL) models to automatically segment NP and NV on ultra-widefield fluorescein angiography (UWFA) images from patients with DR.MethodsRetrospective cross-sectional chart review study. In total, 951 UWFA images were collected from patients with severe non-proliferative DR (NPDR) or proliferative DR (PDR). Each image was segmented and labelled for NP, NV, disc, background and outside areas. Using the labelled images, DL models were trained and validated (80%) using convolutional neural networks (CNNs) for automated segmentation and tested (20%) on test sets. Accuracy of each model and each label were assessed.ResultsThe best accuracy from CNN models for each label was 0.8208, 0.8338, 0.9801, 0.9253 and 0.9766 for NP, NV, disc, background and outside areas, respectively. The best Intersection over Union for each label was 0.6806, 0.5675, 0.7107, 0.8551 and 0.924 and mean mean boundary F1 score (BF score) was 0.6702, 0.8742, 0.9092, 0.8103 and 0.9006, respectively.ConclusionsDL models can detect NV and NP as well as disc and outer margins on UWFA with good performance. This automated segmentation of important UWFA features will aid physicians in DR clinics and in overcoming grader subjectivity.
We analyze the lepton universality violating
B
→
K
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ℓ
+
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-
puzzle
R
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in a model-independent way. The branching ratio
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→
μ
+
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is also considered as a main constraint. We ...show how the branching ratio restricts the allowed region of the parameter space, and investigate new physics effects on the electronic as well as muonic sector in
R
(
K
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. Within a reasonable range of parameters, we find that the new physics scale goes up to
∼
5
TeV, and the new physics effects on the Wilson coefficient are
-
1
≲
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9
N
P
μ
≲
0
.
Neurogenesis in the subgranular zone of the hippocampal dentate gyrus may act as an endogenous repair mechanism in Alzheimer's disease (AD), and the Wnt signaling pathway has been suggested to ...closely modulate neurogenesis in amyloid-β (Aβ)-related AD models. The present study investigated whether mesenchymal stem cells (MSCs) would modulate hippocampal neurogenesis via modulation of the Wnt signaling pathway in a model of AD. In Aβ-treated neuronal progenitor cells (NPCs), the coculture with MSCs increased significantly the expression of Ki-67, GFAP, SOX2, nestin, and HuD compared to Aβ treatment alone. In addition, MSC treatment in Aβ-treated NPCs enhanced the expression of β-catenin and Ngn1 compared to Aβ treatment alone. MSC treatment in Aβ-treated animals significantly increased the number of BrdU-ir cells in the hippocampus at 2 and 4 weeks compared to Aβ treatment alone. In addition, quantitative analysis showed that the number of BrdU and HuD double-positive cells in the dentate gyrus was significantly higher in the MSC-treated group than in controls or after Aβ treatment alone. These results demonstrate that MSC administration significantly augments hippocampal neurogenesis and enhances the differentiation of NPCs into mature neurons in AD models by augmenting the Wnt signaling pathway. The use of MSCs to modulate endogenous adult neurogenesis may have a significant impact on future strategies for AD treatment.
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