Combining individual p-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher's combination test. In modern large-scale data analysis, correlation and sparsity are common features and efficient computation is a necessary requirement for dealing with massive data. To overcome these challenges, we propose a new test that takes advantage of the Cauchy distribution. Our test statistic has a very simple form and is defined as a weighted sum of Cauchy transformation of individual p-values. We prove a non-asymptotic result that the tail of the null distribution of our proposed test statistic can be well approximated by a Cauchy distribution under arbitrary dependency structures. Based on this theoretical result, the p-value calculation of our proposed test is not only accurate, but also as simple as the classic z-test or t-test, making our test well suited for analyzing massive data. We further show that the power of the proposed test is asymptotically optimal in a strong sparsity setting. Extensive simulations demonstrate that the proposed test has both strong power against sparse alternatives and a good accuracy with respect to p-value calculations, especially for very small p-values. The proposed test has also been applied to a genome-wide association study of Crohn's disease and compared with several existing tests.
Figure S1The genomic landscape of sliding windows significantly associated with Lp(a) levels among AAs on chromosome 6. Seven methods are compared: ACAT-V(1,1), ACAT-V(1,25), SKAT(1,1), SKAT(1,25), Burden(1,1), Burden(1,25) and the omnibus test ACAT-O that combines the other six tests, where the two numbers in the parentheses indicate the choice of the beta(MAF) weight parameters and in the test. A dot means that the sliding window at this location is significant by the method that the color of the dot represents. The numbers on the left of the plot show the number of significant windows identified by each method. Figure S2The genomic landscape of sliding windows significantly associated with Lp(a) levels among EAs on chromosome 6. Seven methods are compared: ACAT-V(1,1), ACAT-V(1,25), SKAT(1,1), SKAT(1,25), Burden(1,1), Burden(1,25) and the omnibus test ACAT-O that combines the other six tests, where the two numbers in the parentheses indicate the choice of the beta(MAF) weight parameters and in the test. A dot means that the sliding window at this location is significant by the method that the color of the dot represents. The numbers on the left of the plot show the number of significant windows identified by each method.
Large-scale whole genome sequencing (WGS) studies have enabled the analysis of rare variants (RVs) associated with complex phenotypes. Commonly used RV association tests (RVATs) have limited scope to leverage variant functions. We propose STAAR (variant-Set Test for Association using Annotation infoRmation), a scalable and powerful RVAT method that effectively incorporates both variant categories and multiple complementary annotations using a dynamic weighting scheme. For the latter, we introduce “annotation Principal Components”, multi-dimensional summaries of in silico variant annotations. STAAR accounts for population structure and relatedness, and is scalable for analyzing very large cohort and biobank WGS studies of continuous and dichotomous traits. We applied STAAR to identify RVs associated with four lipid traits in 12,316 discovery samples and 17,822 replication samples from the Trans-Omics for Precision Medicine program. We discovered and replicated novel RV associations, including disruptive missense RVs of NPC1L1 and an intergenic region near APOC1P1 associated with low-density lipoprotein cholesterol.
Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes and effect directions of the causal variants and the choices of weights. Here we propose an Aggregated Cauchy Association Test (ACAT), a general, powerful and computationally efficient p-value combination method to boost power in sequencing studies. First, by combining variant-level p-values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of casual variants in a variant set. Second, by combining different variant set-level p-values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests including the burden test, Sequence Kernel Association Test (SKAT) and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and burden test, and that ACAT-O has a substantially more robust and higher power than the alternative tests.
Objective: To investigate the effects of ibuprofen on cardiac fibrosis in a rat model of type 1 diabetes. Methods: The diabetic model was established by injecting streptozotocin into the rats. Then, ibuprofen or pioglitazone was given by gavage for 8 weeks. The cardiac fibrosis was assessed, and the major components of the renin-angiotensin system, the transforming growth factor β1 (TGF-β1) and the mammalian target of rapamycin (mTOR), were evaluated by histopathological, immunohistochemical, Western blot analysis or ELISA assay. Results: Obvious cardiac fibrosis was detected in the diabetic group and was alleviated by ibuprofen treatment. Angiotensin-converting enzyme (ACE), angiotensin (Ang) II and AngII type 1 receptor (AT1-R) levels were higher, and ACE2, Ang(1-7) and Mas receptor (Mas-R) were lower in the diabetic group. The ratio of ACE to ACE2 was raised in the diabetic group. All these changes were ameliorated by ibuprofen. TGF-β1 and mTOR were raised in the hearts of the diabetic group and were attenuated by ibuprofen treatment. There was no significant difference between the ibuprofen and the pioglitazone groups. Conclusion: Ibuprofen could ameliorate the cardiac fibrosis in diabetic rats by reduction of the ACE/AngII/AT1-R axis and enhancement of the ACE2/Ang(1-7)/Mas-R axis, leading to a decrease in TGF-β1 and mTOR.
The composition and biological activity of donor cells is largely determined by the exosomes they secrete. In this study, we isolated exosomes from young (Young-Exo) and aged (Age-Exo) mesenchymal stem cells (MSCs) and compared their regeneration activity. Young Exo MSCs were more efficient than Aged-Exo at promoting the formation of endothelial tube, reducing fibrosis, and inhibiting apoptosis of cardiomyocytes in vitro; and improving cardiac structure and function in vivo in the hearts of rats following myocardial infarction (MI). MicroRNA sequencing and polymerase chain reaction (PCR) analysis revealed that miR-221-3p was significantly down-regulated in Aged-Exo. The aged MSCs were rejuvenated and their reparative cardiac ability restored when miR-221-3p was overexpressed in Aged-Exo. The protective effect was lost when miR-221-3p expression was knocked down in Young-Exo. These effects of miR-221-3p were achieved through enhancing Akt kinase activity by inhibiting phosphatase and tensin homolog (PTEN). In conclusion, exosomal miR-221-3p secreted from Aged MSCs attenuated the function of angiogenesis and promoted survival of cardiomyocytes. Upregulation of miR-221-3p in aged MSCs improved their ability of angiogenesis, migration and proliferation, and suppressed apoptosis via the PTEN/Akt pathway.
A growing volume of research has used polynomial regression analysis (PRA) to examine congruence effects in a broad range of organizational phenomena. However, conclusions from congruence studies, even ones using the same theoretical framework, vary substantially. We argue that conflicting findings from congruence research can be attributable to several methodological artifacts, including measurement error, collinearity among predictors, and sampling error. These methodological artifacts can significantly affect the estimation accuracy of PRA and undermine the validity of conclusions from primary studies as well as meta-analytic reviews of congruence research. We introduce two alternative methods that address this concern by modeling congruence within a latent variable framework: latent moderated structural equations (LMS) and reliability-corrected single-indicator LMS (SI-LMS). Using a large-scale simulation study with 6,322 conditions and close to 1.9 million replications, we showed how methodological artifacts affected the performance of PRA, specifically, its (un)biasedness, precision, Type I error rate, and power in estimating linear, quadratic, and interaction effects. We also demonstrated the substantial advantages of LMS and SI-LMS compared with PRA in providing accurate and precise estimates, particularly under undesirable conditions. Based on these findings, we discuss how these new methods can help researchers find more consistent effects and draw more meaningful theoretical conclusions in future research. We offer practical recommendations regarding study design, model selection, and sample size planning. In addition, we provide example syntax to facilitate the application of LMS and SI-LMS in congruence research.
Background Exosome transplantation is a promising cell-free therapeutic approach for the treatment of ischemic heart disease. The purpose of this study was to explore whether exosomes derived from Macrophage migration inhibitory factor (MIF) engineered umbilical cord MSCs (ucMSCs) exhibit superior cardioprotective effects in a rat model of AMI and reveal the mechanisms underlying it. Results Exosomes isolated from ucMSCs (MSC-Exo), MIF engineered ucMSCs (MIF-Exo) and MIF downregulated ucMSCs (siMIF-Exo) were used to investigate cellular protective function in human umbilical vein endothelial cells (HUVECs) and H9C2 cardiomyocytes under hypoxia and serum deprivation (H/SD) and infarcted hearts in rats. Compared with MSC-Exo and siMIF-Exo, MIF-Exo significantly enhanced proliferation, migration, and angiogenesis of HUVECs and inhibited H9C2 cardiomyocyte apoptosis under H/SD in vitro. MIF-Exo also significantly inhibited cardiomyocyte apoptosis, reduced fibrotic area, and improved cardiac function as measured by echocardiography in infarcted rats in vivo. Exosomal miRNAs sequencing and qRT-PCR confirmed miRNA-133a-3p significantly increased in MIF-Exo. The biological effects of HUVECs and H9C2 cardiomyocytes were attenuated with incubation of MIF-Exo and miR-133a-3p inhibitors. These effects were accentuated with incubation of siMIF-Exo and miR-133a-3p mimics that increased the phosphorylation of AKT protein in these cells. Conclusion MIF-Exo can provide cardioprotective effects by promoting angiogenesis, inhibiting apoptosis, reducing fibrosis, and preserving heart function in vitro and in vivo. The mechanism in the biological activities of MIF-Exo involves miR-133a-3p and the downstream AKT signaling pathway.
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