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Local genetic correlation quantifies the genetic similarity of complex traits in specific genomic regions. However, accurate estimation of local genetic correlation remains challenging, due to linkage disequilibrium in local genomic regions and sample overlap across studies. We introduce SUPERGNOVA, a statistical framework to estimate local genetic correlations using summary statistics from genome-wide association studies. We demonstrate that SUPERGNOVA outperforms existing methods through simulations and analyses of 30 complex traits. In particular, we show that the positive yet paradoxical genetic correlation between autism spectrum disorder and cognitive performance could be explained by two etiologically distinct genetic signatures with bidirectional local genetic correlations.

Structural variations of the human brain are heritable and highly polygenic traits, with hundreds of associated genes identified in recent genome-wide association studies (GWAS). Transcriptome-wide association studies (TWAS) can both prioritize these GWAS findings and also identify additional gene-trait associations. Here we perform cross-tissue TWAS analysis of 211 structural neuroimaging and discover 278 associated genes exceeding Bonferroni significance threshold of 1.04 × 10 −8 . The TWAS-significant genes for brain structures have been linked to a wide range of complex traits in different domains. Through TWAS gene-based polygenic risk scores (PRS) prediction, we find that TWAS PRS gains substantial power in association analysis compared to conventional variant-based GWAS PRS, and up to 6.97% of phenotypic variance (p-value = 7.56 × 10 −31 ) can be explained in independent testing data sets. In conclusion, our study illustrates that TWAS can be a powerful supplement to traditional GWAS in imaging genetics studies for gene discovery-validation, genetic co-architecture analysis, and polygenic risk prediction. Brain structural traits are highly heritable and have been linked to disease. Here the authors have used gene expression data to perform a transcriptome-wide association study on 211 brain structural traits, discovering 273 associated genes.

The epidermal growth factor receptor (EGFR) is frequently mutated in human cancer 1 , 2 , and is an important therapeutic target. EGFR inhibitors have been successful in lung cancer, where mutations in the intracellular tyrosine kinase domain activate the receptor 1 , but not in glioblastoma multiforme (GBM) 3 , where mutations occur exclusively in the extracellular region. Here we show that common extracellular GBM mutations prevent EGFR from discriminating between its activating ligands 4 . Different growth factor ligands stabilize distinct EGFR dimer structures 5 that signal with different kinetics to specify or bias outcome 5 , 6 . EGF itself induces strong symmetric dimers that signal transiently to promote proliferation. Epiregulin (EREG) induces much weaker asymmetric dimers that drive sustained signalling and differentiation 5 . GBM mutations reduce the ability of EGFR to distinguish EREG from EGF in cellular assays, and allow EGFR to form strong (EGF-like) dimers in response to EREG and other low-affinity ligands. Using X-ray crystallography, we further show that the R84K GBM mutation symmetrizes EREG-driven extracellular dimers so that they resemble dimers normally seen with EGF. By contrast, a second GBM mutation, A265V, remodels key dimerization contacts to strengthen asymmetric EREG-driven dimers. Our results argue for an important role of altered ligand discrimination by EGFR in GBM, with potential implications for therapeutic targeting. Extracellular glioblastoma-associated mutations reduce the ability of the epidermal growth factor receptor to distinguish between its ligands.

In this paper, statistics of ice thickness and ice strength of first-year sea ice along the Northern Sea Route (NSR) is studied to provide useful information for the design and operation of Arctic ships. Specifically, ice thickness, ice strength and other physical parameters of the sea ice are estimated. Four representative sites are selected to study the ice environment and ice strength in different sea areas along the NSR during the ice growth season. Besides that, a good knowledge of the co-variation relationships between these ice parameters in a particular region would promote the estimation of ice loads acting on structures located in this region. In this work, a novel probabilistic model is introduced to describe the probability distribution of the ice flexural strength. The co-variation relationships between ice thickness and ice strength are quantified in terms of correlation coefficients. The influence of air temperature on ice properties is also investigated and discussed.

Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8 + T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.

This study was conceived to explore the role and the mechanism of Loureirin B (LB) in hepatic IRI. The viability of LB-treated AML-12 cells was assessed using CCK-8 assay and inflammatory cytokines were detected using ELISA. The activities of ROS and oxidative stress markers MDA, SOD, and GSH-Px were detected using DCFH-DA and corresponding assay kits. The cell apoptosis and caspase3 activity were estimated with flow cytometry and caspase3 assay kits. The expressions of arachidonate 5-lipoxygenase (ALOX5) and apoptosis- and mitochondrial dynamics–related proteins were detected using western blot. The interaction between LB and ALOX5 was analyzed with molecular docking. The transfection efficacy of oe-ALOX5 was examined with RT-qPCR and western blot. Mitochondrial membrane potential was detected with JC-1 staining and immunofluorescence (IF) assay was employed to estimate mitochondrial fusion and fission. The present work found that LB revived the viability, inhibited inflammatory response, suppressed oxidative stress, repressed the apoptosis, and maintained mitochondrial homeostasis in H/R-induced AML-12 cells, which were all reversed by ALOX5 overexpression. Collectively, LB regulated mitochondrial homeostasis by downregulating ALOX5, thereby improving hepatic IRI.

Developing ecological approaches for disease control is critical for future sustainable aquaculture development. White spot syndrome (WSS), caused by white spot syndrome virus (WSSV), is the most severe disease in cultured shrimp production. Culturing specific pathogen-free (SPF) broodstock is an effective and widely used strategy for controlling WSS. However, most small-scale farmers, who predominate shrimp aquaculture in developing countries, cannot cultivate SPF shrimp, as they do not have the required infrastructure and skills. Thus, these producers are more vulnerable to WSS outbreaks than industrial farms. Here we developed a shrimp polyculture system that prevents WSS outbreaks by introducing specific fish species. The system is easy to implement and requires no special biosecurity measures. The promotion of this system in China demonstrated that it allowed small-scale farmers to improve their livelihood through shrimp cultivation by controlling WSS outbreaks and increasing the production of ponds.Wang et al. develop a shrimp polyculture system that prevents white spot syndrome outbreaks by introducing specific fish species. The system is easy to implement and requires no special biosecurity measures which makes it ideal for small-scale farmers.

This study presents a simplified analytical model for predicting the structural responses of double-bottom ships in a shoal grounding scenario. This solution is based on a series of analytical models developed from elastic-plastic mechanism theories for different structural components, including bottom girders, floors, bottom plating, and attached stiffeners. We verify this simplified analytical model by numerical simulation, and establish finite element models for a typical tanker hold and a rigid indenter representing seabed obstacles. Employing the LS-DYNA finite element solver, we conduct numerical simulations for shoal-grounding cases with a wide range of slope angles and indentation depths. In comparison with numerical simulations, we verify the proposed simplified analytical model with respect to the total energy dissipation and the horizontal grounding resistance. We also investigate the interaction effect of deformation patterns between bottom structure components. Our results show that the total energy dissipation and resistances predicted by the analytical model agree well with those from numerical simulations.

Age is the dominant risk factor for infectious diseases, but the mechanisms linking age to infectious disease risk are incompletely understood. Age-related mosaic chromosomal alterations (mCAs) detected from genotyping of blood-derived DNA, are structural somatic variants indicative of clonal hematopoiesis, and are associated with aberrant leukocyte cell counts, hematological malignancy, and mortality. Here, we show that mCAs predispose to diverse types of infections. We analyzed mCAs from 768,762 individuals without hematological cancer at the time of DNA acquisition across five biobanks. Expanded autosomal mCAs were associated with diverse incident infections (hazard ratio (HR) 1.25; 95% confidence interval (CI) = 1.15–1.36; P  = 1.8 × 10 −7 ), including sepsis (HR 2.68; 95% CI = 2.25–3.19; P  = 3.1 × 10 −28 ), pneumonia (HR 1.76; 95% CI = 1.53–2.03; P  = 2.3 × 10 −15 ), digestive system infections (HR 1.51; 95% CI = 1.32–1.73; P  = 2.2 × 10 −9 ) and genitourinary infections (HR 1.25; 95% CI = 1.11–1.41; P  = 3.7 × 10 −4 ). A genome-wide association study of expanded mCAs identified 63 loci, which were enriched at transcriptional regulatory sites for immune cells. These results suggest that mCAs are a marker of impaired immunity and confer increased predisposition to infections. The burden of mosaic chromosomal alterations in blood-derived DNA, a type of clonal hematopoiesis, is associated with an increased risk for diverse types of infections, including sepsis and pneumonia.

With the advancement of high-throughput sequencing and massively parallel technologies, more and more omics data are available for biomedical research. Genomics, transcriptomics, proteomics, metabolomics and microbiomics data help biomedical researchers delve into the complex biological systems and dissect the underlying mechanisms of genetics from different perspectives. Despite the huge success in molecular biology research, the potential value of omics data has not been fully realized, especially for treatment discovery. This thesis is focused on the computational method development for biomarker identification, drug repositioning, and polypharmacy risk assessment, which are three substantial tasks in treatment discovery and development. The first contribution of this thesis is development of a novel transcriptome-wide association analysis method for biomarker identification. Transcriptome-wide association studies (TWAS) have several advantages over traditional genomewide association studies (GWAS) because it performs gene-level association tests by which it reduces the multiple testing burden and outputs interpretable association results. In this project, we developed a novel TWAS method based on joint bounded-variable least-squares. We trained our expression imputation models with genotype and RNA-sequencing data from the updated version of Genotype-Tissue Expression (GTEx) project and the imputation accuracy outperformed other state-of-the-art methods. For the transcripts of interest, we incorporated non-coding transcripts in our analysis pipeline by performing specific expression adjustment procedures. For association test, we performed TWAS for a number of traits based on their GWAS summary statistics and identified novel genes with significant associations.The second contribution of this thesis is a computational drug repositioning framework based on multiomics data. Drug discovery is challenging due to its long research cycle and high capital investment, while computational drug repositioning seems to be a cost-effective alternative for de novo drug discovery. In this project, we proposed a signature-matching-based drug repositioning framework with multiple gene expression studies and TWAS. The disease signature was curated from reversible genes identified through differential expression analysis, network analysis, pathway analysis as well as TWAS. By matching disease signature with the cell-line-specific drug signatures in perturbation database, repurposed drug candidates can be identified through connectivity scores. We implemented this framework to discover repurposed drugs for non-alcoholic steatohepatitis. The validation experiments showed that four out of seven top recommended drug candidates have in vitro lipid reduction efficacy.The third contribution of this thesis is development and validation of a polypharmacy burden score based on adverse drug-drug interactions. Polypharmacy is a common phenomenon in older population and has been associated with increased risk of adverse health outcomes. One of the main reasons for the increased risk is the increased drug-drug interactions (DDI). In this project, we trained a new drug-drug interaction prediction models and built a novel polypharmacy burden score with ensemble information from knowledge base and prediction algorithms. The multi-class classification algorithm for DDI was trained on known DDI and curated empirical safe co-prescription pairs. The polypharmacy burden score was validated in the veterans aging cohort study (VACS) EHR database by performing association tests for hospitalization and comparing the FIB-4 index and eGFR for high-burden-score and low-burden-score cohorts.