A clustering linear combination method for multiple phenotype association studies based on GWAS summary statistics

There is strong evidence showing that joint analysis of multiple phenotypes in genome-wide association studies (GWAS) can increase statistical power when detecting the association between genetic variants and human complex diseases. We previously developed the Clustering Linear Combination (CLC) method and a computationally efficient CLC (ceCLC) method to test the association between multiple phenotypes and a genetic variant, which perform very well. However, both of these methods require individual-level genotypes and phenotypes that are often not easily accessible. In this research, we develop a novel method called sCLC for association studies of multiple phenotypes and a genetic variant based on GWAS summary statistics. We use the LD score regression to estimate the correlation matrix among phenotypes. The test statistic of sCLC is constructed by GWAS summary statistics and has an approximate Cauchy distribution. We perform a variety of simulation studies and compare sCLC with other commonly used methods for multiple phenotype association studies using GWAS summary statistics. Simulation results show that sCLC can control Type I error rates well and has the highest power in most scenarios. Moreover, we apply the newly developed method to the UK Biobank GWAS summary statistics from the XIII category with 70 related musculoskeletal system and connective tissue phenotypes. The results demonstrate that sCLC detects the most number of significant SNPs, and most of these identified SNPs can be matched to genes that have been reported in the GWAS catalog to be associated with those phenotypes. Furthermore, sCLC also identifies some novel signals that were missed by standard GWAS, which provide new insight into the potential genetic factors of the musculoskeletal system and connective tissue phenotypes

Gene-based association tests using GWAS summary statistics and incorporating eQTL

Although genome-wide association studies (GWAS) have been successfully applied to a variety of complex diseases and identified many genetic variants underlying complex diseases via single marker tests, there is still a considerable heritability of complex diseases that could not be explained by GWAS. One alternative approach to overcome the missing heritability caused by genetic heterogeneity is gene-based analysis, which considers the aggregate effects of multiple genetic variants in a single test. Another alternative approach is transcriptome-wide association study (TWAS). TWAS aggregates genomic information into functionally relevant units that map to genes and their expression. TWAS is not only powerful, but can also increase the interpretability in biological mechanisms of identified trait associated genes. In this study, we propose a powerful and computationally efficient gene-based association test, called Overall. Using extended Simes procedure, Overall aggregates information from three types of traditional gene-based association tests and also incorporates expression quantitative trait locus (eQTL) information into a gene-based association test using GWAS summary statistics. We show that after a small number of replications to estimate the correlation among the integrated gene-based tests, the p values of Overall can be calculated analytically. Simulation studies show that Overall can control type I error rates very well and has higher power than the tests that we compared with. We also apply Overall to two schizophrenia GWAS summary datasets and two lipids GWAS summary datasets. The results show that this newly developed method can identify more significant genes than other methods we compared with

Integrating External Controls by Regression Calibration for Genome-Wide Association Study

Genome-wide association studies (GWAS) have successfully revealed many disease-associated genetic variants. For a case-control study, the adequate power of an association test can be achieved with a large sample size, although genotyping large samples is expensive. A cost-effective strategy to boost power is to integrate external control samples with publicly available genotyped data. However, the naive integration of external controls may inflate the type I error rates if ignoring the systematic differences (batch effect) between studies, such as the differences in sequencing platforms, genotype-calling procedures, population stratification, and so forth. To account for the batch effect, we propose an approach by integrating External Controls into the Association Test by Regression Calibration (iECAT-RC) in case-control association studies. Extensive simulation studies show that iECAT-RC not only can control type I error rates but also can boost statistical power in all models. We also apply iECAT-RC to the UK Biobank data for M72 Fibroblastic disorders by considering genotype calling as the batch effect. Four SNPs associated with fibroblastic disorders have been detected by iECAT-RC and the other two comparison methods, iECAT-Score and Internal. However, our method has a higher probability of identifying these significant SNPs in the scenario of an unbalanced case-control association study

Experimental study on flue gas foam-assisted steam flooding: investigating characteristics of enhanced oil recovery and gas storage

Steam flooding is one of the most widely used heavy oil thermal recovery technologies. Steam transfers heat to heavy oil to reduce viscosity and improve fluidity. The current problem is that steam loses a lot of heat in the formation, and there are serious carbon emissions in the whole production process. In this paper, flue gas and steam were combined to drive heavy oil in the form of composite thermal fluid, and foam was added on this basis. With the help of one-dimensional sandpack model, both single-model and parallel dual-model with permeability ratio experiments were conducted to investigate key characteristics such as steam heat transfer, heavy oil production and flue gas retention during the displacement process. The experimental results indicated that flue gas effectively inhibited steam condensation and reduced heat loss during the flow process. Compared to steam flooding, the sandpack model exhibited temperature rises of 4.4°C and 9.1°C at the middle and end, respectively. While flue gas foam fell slightly short of flue gas in terms of enhanced heat transfer, it outperforms in recovery factor, achieving a 10.4% improvement over flue gas-assisted steam flooding. The foam blocked gas channeling by accumulating and capturing along the flow path, resulting in a gas retention volume of 389 mL within the model. Furthermore, the flue gas foam facilitated steam flow to previously unswept low-permeability areas, thus enhancing oil recovery. In the parallel double-model experiment, the low-permeability model exhibited significantly improved oil displacement efficiency compared to flue gas-assisted steam flooding, and the remaining oil content in the end of the high permeability model was increased by 1.9%, while the remaining oil content in the front and end of the low-permeability model was reduced by 3.5% and 3.8% respectively

Spred2 controls the severity of Concanavalin A-induced liver damage by limiting interferon-gamma production by CD4(+) and CD8(+) T cells

Introduction: Mitogen-activated protein kinases (MAPKs) are involved in T cell-mediated liver damage. However, the inhibitory mechanism(s) that controls T cell-mediated liver damage remains unknown. Objectives: We investigated whether Spred2 (Sprouty-related, EVH1 domain-containing protein 2) that negatively regulates ERK-MAPK pathway has a biological impact on T cell-mediated liver damage by using a murine model. Methods: We induced hepatotoxicity in genetically engineered mice by intravenously injecting Concanavalin A (Con A) and analyzed the mechanisms using serum chemistry, histology, ELISA, qRT-PCR, Western blotting and flow cytometry. Results: Spred2-deficient mice (Spred2(-/-)) developed more sever liver damage than wild-type (WT) mice with increased interferon-gamma (IFNy) production. Hepatic ERK phosphorylation was enhanced in Spred2(-/-) mice, and pretreatment of Spred2(-/-) mice with the MAPK/ERK inhibitor U0126 markedly inhibited the liver damage and reduced IFN gamma production. Neutralization of IFNy abolished the damage with decreased hepatic Stat1 activation in Spred2(-/-) mice. IFN gamma was mainly produced from CD4(+) and CD8(+) T cells, and their depletion decreased liver damage and IFN gamma production. Transplantation of CD4(+) and/or CD8(+) T cells from Spred2(-/-) mice into RAG1(-/-) mice deficient in both T and B cells caused more severe liver damage than those from WT mice. Hepatic expression of T cell attractants, CXCL9 and CXCL10, was augmented in Spred2(-/-) mice as compared to WT mice. Conversely, liver damage, IFN gamma production and the recruitment of CD4(+) and CD8(+) T cells in livers after Con A challenge were lower in Spred2 transgenic mice, and Spred2-overexpressing CD4(+) and CD8(+) T cells produced lower levels of IFN gamma than WT cells upon stimulation with Con A in vitro. Conclusion: We demonstrated, for the first time, that Spred2 functions as an endogenous regulator of T cell IFNy production and Spred2-mediated inhibition of ERK-MAPK pathway may be an effective remedy for T cell-dependent liver damage

A New Hip Fracture Risk Index Derived from FEA-Computed Proximal Femur Fracture Loads and Energies-to-Failure

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient specific finite element analysis (FEA) computes the force (fracture load) to break the proximal femur in a particular loading condition. It provides different structural information about the proximal femur that can influence a subject overall fracture risk. To obtain a more robust measure of fracture risk, we used principal component analysis (PCA) to develop a global FEA computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies to failure in four loading conditions (single-limb stance and impact from a fall onto the posterior, posterolateral, and lateral aspects of the greater trochanter) of 110 hip fracture subjects and 235 age and sex matched control subjects from the AGES-Reykjavik study. We found that the first PC (PC1) of the FE parameters was the only significant predictor of hip fracture. Using a logistic regression model, we determined if prediction performance for hip fracture using PC1 differed from that using FE parameters combined by stratified random resampling with respect to hip fracture status. The results showed that the average of the area under the receive operating characteristic curve (AUC) using PC1 was always higher than that using all FE parameters combined in the male subjects. The AUC of PC1 and AUC of the FE parameters combined were not significantly different than that in the female subjects or in all subjectsComment: 27 pages, 4 figure

Simian Immunodeficiency Virus Infection Mediated Changes in Jejunum and Peripheral SARS-CoV-2 Receptor ACE2 and Associated Proteins or Genes in Rhesus Macaques

Angiotensin converting enzyme-2 (ACE2) and associated proteins play a pivotal role in various physiological and pathological events, such as immune activation, inflammation, gut barrier maintenance, intestinal stem cell proliferation, and apoptosis. Although many of these clinical events are quite significant in SIV/HIV infection, expression profiling of these proteins has not been well reported. Considering the different pathological consequences in the gut after HIV infection, we hypothesized that the expression of ACE2 and associated proteins of the Renin-angiotensin system (RAS) could be compromised after SIV/HIV infection. We quantified the gene expression of ACE2 as well as AGTR1/2, ADAM17, and TMPRSS2, and compared between SIV infected and uninfected rhesus macaques (Macaca mulatta; hereafter abbreviated RMs). The gene expression analysis revealed significant downregulation of ACE2 and upregulation of AGTR2 and inflammatory cytokine IL-6 in the gut of infected RMs. Protein expression profiling also revealed significant upregulation of AGTR2 after infection. The expression of ACE2 in protein level was also decreased, but not significantly, after infection. To understand the entirety of the process in newly regenerated epithelial cells, a global transcriptomic study of enteroids raised from intestinal stem cells was performed. Interestingly, most of the genes associated with the RAS, such as DPP4, MME, ANPEP, ACE2, ENPEP, were found to be downregulated in SIV infection. HNFA1 was found to be a key regulator of ACE2 and related protein expression. Jejunum CD4+ T cell depletion and increased IL-6 mRNA, MCP-1 and AGTR2 expression may signal inflammation, monocyte/macrophage accumulation and epithelial apoptosis in accelerating SIV pathogenesis. Overall, the findings in the study suggested a possible impact of SIV/HIV infection on expression of ACE2 and RAS-associated proteins resulting in the loss of gut homeostasis. In the context of the current COVID-19 pandemic, the outcome of SARS-CoV-2 and HIV co-infection remains uncertain and needs further investigation as the significance profile of ACE2, a viral entry receptor for SARS-CoV-2, and its expression in mRNA and protein varied in the current study. There is a concern of aggravated SARS-CoV-2 outcomes due to possible serious pathological events in the gut resulting from compromised expression of RAS- associated proteins in SIV/HIV infection

Multi-view information fusion using multi-view variational autoencoders to predict proximal femoral strength

The aim of this paper is to design a deep learning-based model to predict proximal femoral strength using multi-view information fusion. Method: We developed new models using multi-view variational autoencoder (MVAE) for feature representation learning and a product of expert (PoE) model for multi-view information fusion. We applied the proposed models to an in-house Louisiana Osteoporosis Study (LOS) cohort with 931 male subjects, including 345 African Americans and 586 Caucasians. With an analytical solution of the product of Gaussian distribution, we adopted variational inference to train the designed MVAE-PoE model to perform common latent feature extraction. We performed genome-wide association studies (GWAS) to select 256 genetic variants with the lowest p-values for each proximal femoral strength and integrated whole genome sequence (WGS) features and DXA-derived imaging features to predict proximal femoral strength. Results: The best prediction model for fall fracture load was acquired by integrating WGS features and DXA-derived imaging features. The designed models achieved the mean absolute percentage error of 18.04%, 6.84% and 7.95% for predicting proximal femoral fracture loads using linear models of fall loading, nonlinear models of fall loading, and nonlinear models of stance loading, respectively. Compared to existing multi-view information fusion methods, the proposed MVAE-PoE achieved the best performance. Conclusion: The proposed models are capable of predicting proximal femoral strength using WGS features and DXA-derived imaging features. Though this tool is not a substitute for FEA using QCT images, it would make improved assessment of hip fracture risk more widely available while avoiding the increased radiation dosage and clinical costs from QCT.Comment: 16 pages, 3 figure

Spred-2 deficiency exacerbates acetaminophen-induced hepatotoxicity in mice

MAPKs are involved in acetaminophen (APAP)-hepatotoxicity, but the regulatory mechanism remains unknown. Here, we explored the role of Spred-2 that negatively regulates Ras/ERK pathway in APAP-hepatotoxicity. Spred-2 knockout (KO) mice demonstrated exacerbated liver injury, an event that was associated with increased numbers of CD4(+) T, CD8(+) T and NK cells in the liver compared to the control. Levels of CXCL9/CXCL10 that attract and activate these cells were increased in Spred-2 KO-liver. Kupffer cells isolated from Spred-2 KO mice after APAP challenge expressed higher levels of CXCL9/CXCL10 than those from the control. Upon stimulation with APAP or IFN gamma, naive Kupffer cells from Spred-2 KO mice expressed higher levels of CXCL9/CXCL10. NK cell-depletion attenuated APAP-hepatotoxicity with lowered hepatic IFN gamma and decreased numbers of not only NK cells but also CD4(+) T and CD8(+) T cells in the liver. These results suggest that Spred-2 negatively regulates APAP-hepatotoxicity under the control of Kupffer cells and NK cells