20 research outputs found
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
The Prognostic Value of Plasma Soluble ST2 in Hospitalized Chinese Patients with Heart Failure
<div><p>Background</p><p>sST2 has been shown to be a risk predictor in heart failure (HF). Our aim was to explore the characteristics and prognostic value of soluble ST2 (sST2) in hospitalized Chinese patients with HF.</p><p>Methods and Results</p><p>We consecutively enrolled 1528 hospitalized patients with HF. Receiver operating characteristic (ROC) and multivariable Cox proportional hazards analysis were used to assess the prognostic values of sST2. Adverse events were defined as all-cause death and cardiac transplantation. During a median follow-up of 19.1 months, 325 patients experienced adverse events. Compared with patients free of events, sST2 concentrations were significantly higher in patients with events (<i>P</i><0.001). Univariable and multivariable Cox regression analyses showed sST2 concentrations were significantly associated with adverse events (per 1 log unit, adjusted hazard ratio 1.52, 95% confidence interval: 1.30 to 1.78, <i>P</i><0.001). An sST2 concentration in the highest quartiles (>55.6 ng/mL) independently predicted events in comparison to the lowest quartile (≤25.2 ng/mL) when adjusted by multivariable model. In ROC analysis, the area under the curve for sST2 was not different from that for NT-proBNP in short and longer term. Over time, sST2 also improved discrimination and reclassification of risk beyond NT-proBNP.</p><p>Conclusions</p><p>sST2 is a strong independent risk predictor in Chinese patients hospitalized with HF and can significantly provide additional prognostic value to NT-proBNP in risk prediction.</p></div
Univariable and multivariable Cox regression analysis for predicting all-cause death and transplantation.
<p>Univariable and multivariable Cox regression analysis for predicting all-cause death and transplantation.</p
Values of sST2 and associations with left ventricular ejection fraction as a function of New York Heart Association functional class.
<p><i>P</i> values indicated the differences among groups stratified by left ventricular ejection fraction.</p
Baseline characteristics of study populations according to outcome.
<p>Baseline characteristics of study populations according to outcome.</p
Rate of all-cause death or cardiac transplantation according to sST2 quartiles at 3 months (<i>P</i><0.001 for trend), 1 year (<i>P</i><0.001 for trend) and 3 years (<i>P</i><0.001 for trend).
<p>Rate of all-cause death or cardiac transplantation according to sST2 quartiles at 3 months (<i>P</i><0.001 for trend), 1 year (<i>P</i><0.001 for trend) and 3 years (<i>P</i><0.001 for trend).</p
Improvement of sST2 to NT-proBNP for predicting all-cause death and transplantation according to follow-up time.
<p>Improvement of sST2 to NT-proBNP for predicting all-cause death and transplantation according to follow-up time.</p
Kaplan-Meier survival curves for all-cause death or cardiac transplantation according to (A) sST2 quartiles, and (B) sST2 median for all patients.
<p><i>P</i> values indicated the differences among groups.</p
The values of sST2 and NT-proBNP for predicting all-cause death and transplantation according to follow-up time.
<p>The values of sST2 and NT-proBNP for predicting all-cause death and transplantation according to follow-up time.</p
Hazard Ratios for the association between sST2 quartiles and all-cause death or cardiac transplantation according to follow-up time.
<p>(A) 1 month; (B) 3 month; (C) 6 month; (D) 1 year; (E) 2 year; and (F) 3 year. Multivariable Cox regression analyses were performed to obtain hazard ratios. Patients in the lowest quartiles were used as reference. Patients with the highest quartiles showed significant hazard ratio for all-cause death or cardiac transplantation in comparison with the patients with the first quartile after adjustment for clinical risk factors (all <i>P</i> value <0.001).</p