Joint analysis of SNP and gene expression data in genetic association studies of complex diseases

Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease, for example, gene expressions, are usually neglected in these association studies. In this paper, we propose to exploit gene expression information to more powerfully test the association between SNPs and diseases by jointly modeling the relations among SNPs, gene expressions and diseases. We propose a variance component test for the total effect of SNPs and a gene expression on disease risk. We cast the test within the causal mediation analysis framework with the gene expression as a potential mediator. For eQTL SNPs, the use of gene expression information can enhance power to test for the total effect of a SNP-set, which is the combined direct and indirect effects of the SNPs mediated through the gene expression, on disease risk. We show that the test statistic under the null hypothesis follows a mixture of $\chi^2$ distributions, which can be evaluated analytically or empirically using the resampling-based perturbation method. We construct tests for each of three disease models that are determined by SNPs only, SNPs and gene expression, or include also their interactions. As the true disease model is unknown in practice, we further propose an omnibus test to accommodate different underlying disease models. We evaluate the finite sample performance of the proposed methods using simulation studies, and show that our proposed test performs well and the omnibus test can almost reach the optimal power where the disease model is known and correctly specified. We apply our method to reanalyze the overall effect of the SNP-set and expression of the ORMDL3 gene on the risk of asthma.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS690 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Gene set analysis using variance component tests

Background: Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. Results: We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). Conclusion: We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data

Causal Inference in the Multiverse of Hazard

Hazard serves as a pivotal estimand in both practical applications and methodological frameworks. However, its causal interpretation poses notable challenges, including inherent selection biases and ill-defined populations to be compared between different treatment groups. In response, we propose a novel definition of counterfactual hazard within the framework of possible worlds. Instead of conditioning on prior survival status as a conditional probability, our new definition involves intervening in the prior status, treating it as a marginal probability. Using single-world intervention graphs, we demonstrate that the proposed counterfactual hazard is a type of controlled direct effect. Conceptually, intervening in survival status at each time point generates a new possible world, where the proposed hazards across time points represent risks in these hypothetical scenarios, forming a "multiverse of hazard." The cumulative and average counterfactual hazards correspond to the sum and average of risks across this multiverse, respectively, with the actual world's risk lying between the two. This conceptual shift reframes hazards in the actual world as a collection of risks across possible worlds, marking a significant advancement in the causal interpretation of hazards

Phosphoenolpyruvate Regulates the JunB-Dependent Pathogenic Th17 Transcriptional Program

Aerobic glycolysis, a metabolic pathway essential for effector T cell survival and proliferation, regulates the differentiation of autoimmune T helper (Th)17 cells, but the mechanism underlying this regulation is largely unknown. Here, we identify a glycolytic intermediate metabolite, phosphoenolpyruvate (PEP), as a negative regulator of Th17 differentiation. PEP supplementation or inhibition of downstream glycolytic enzymes in differentiating Th17 cells increases intracellular PEP levels and inhibits the expression of Th17 signature molecules, such as IL-17A. However, PEP supplementation does not significantly affect metabolic reprogramming, cell proliferation, and survival of differentiating Th17 cells. Mechanistically, PEP regulates the JunB-dependent pathogenic Th17 transcriptional program by inhibiting the DNA-binding activity of the JunB/BATF/IRF4 complex. Furthermore, daily administration of PEP to mice inhibits the generation of Th17 cells and ameliorates Th17-dependent autoimmune encephalomyelitis. These data demonstrate that PEP links aerobic glycolysis to the JunB-dependent pathogenic Th17 transcriptional program, suggesting the therapeutic potential of PEP for autoimmune diseases.Okinawa Institute of Science and Technology Graduate Universit

TEGS-CN: A Statistical Method for Pathway Analysis of Genome-wide Copy Number Profile

The effects of copy number alterations make up a significant part of the tumor genome profile, but pathway analyses of these alterations are still not well established. We proposed a novel method to analyze multiple copy numbers of genes within a pathway, termed Test for the Effect of a Gene Set with Copy Number data (TEGS-CN). TEGS-CN was adapted from TEGS, a method that we previously developed for gene expression data using a variance component score test. With additional development, we extend the method to analyze DNA copy number data, accounting for different sizes and thus various numbers of copy number probes in genes. The test statistic follows a mixture of X2 distributions that can be obtained using permutation with scaled X2 approximation. We conducted simulation studies to evaluate the size and the power of TEGS-CN and to compare its performance with TEGS. We analyzed a genome-wide copy number data from 264 patients of non-small-cell lung cancer. With the Molecular Signatures Database (MSigDB) pathway database, the genome-wide copy number data can be classified into 1814 biological pathways or gene sets. We investigated associations of the copy number profile of the 1814 gene sets with pack-years of cigarette smoking. Our analysis revealed five pathways with significant P values after Bonferroni adjustment (<2.8 × 10−5), including the PTEN pathway (7.8 × 10−7), the gene set up-regulated under heat shock (3.6 × 10−6), the gene sets involved in the immune profile for rejection of kidney transplantation (9.2 × 10−6) and for transcriptional control of leukocytes (2.2 × 10−5), and the ganglioside biosynthesis pathway (2.7 × 10−5). In conclusion, we present a new method for pathway analyses of copy number data, and causal mechanisms of the five pathways require further study

General approach of causal mediation analysis with causally ordered multiple mediators and survival outcome

Causal mediation analysis with multiple mediators (causal multi-mediation analysis) is critical in understanding why an intervention works, especially in medical research. Deriving the path-specific effects (PSEs) of exposure on the outcome through a certain set of mediators can detail the causal mechanism of interest. However, the existing models of causal multi-mediation analysis are usually restricted to partial decomposition, which can only evaluate the cumulative effect of several paths. Moreover, the general form of PSEs for an arbitrary number of mediators has not been proposed. In this study, we provide a generalized definition of PSE for partial decomposition (partPSE) and for complete decomposition, which are extended to the survival outcome. We apply the interventional analogues of PSE (iPSE) for complete decomposition to address the difficulty of non-identifiability. Based on Aalen’s additive hazards model and Cox’s proportional hazards model, we derive the generalized analytic forms and illustrate asymptotic property for both iPSEs and partPSEs for survival outcome. The simulation is conducted to evaluate the performance of estimation in several scenarios. We apply the new methodology to investigate the mechanism of methylation signals on mortality mediated through the expression of three nested genes among lung cancer patients

Ultra-broad and sharp-transition bandpass terahertz filters by hybridizing multiple resonances mode in monolithic metamaterials

We present three monolithic metamaterial-based THz bandpass filters, the skewed circular slot rings, meandered slots and Jerusalem cross slots, to fit in the THz gap. These THz bandpass filters are comprised of a metal-dielectric-metal (MDM) structure that supports multiple resonances of electric dipole, magnetic dipole, and standing-wave-like modes. By exciting and further hybridizing these individual resonance modes, we demonstrate excellent performance of broad bandwidth and sharp band-edge transition beyond conventional bandpass filters. By further employing our ad hoc Genetic Algorithm (GA) and Periodic Method of Moments (PMM) to optimize our designs, we achieve an ultra-broad 3dB fractional bandwidth and sharp band-edge transition up to 82.2% and 58.3 dB/octave, respectively, benefiting the practical applications such as material recognition in security systems, imaging, and absorbers

Decreased Risk of Osteoporosis Incident in Subjects Receiving Chinese Herbal Medicine for Sjögren Syndrome Treatment: A Retrospective Cohort Study with a Nested Case-Control Analysis

Sjögren syndrome (SS) is a long-lasting inflammatory autoimmune disease that may cause diverse manifestations, particularly osteoporosis. Though usage of Chinese herbal medicine (CHM) can safely manage autoimmune disease and treatment-related symptoms, the relation between CHM use and osteoporosis risk in SS persons is not yet recognized. With that in mind, this population-level nested case-control study aimed to compare the risk of osteoporosis with and without CHM use. Potential subjects aged 20–70 years, diagnosed with SS between 2001 and 2010, were retrieved from a national health claims database. Those diagnosed with osteoporosis after SS were identified and randomly matched to those without osteoporosis. We capitalize on the conditional logistic regression to estimate osteoporosis risk following CHM use. A total of 1240 osteoporosis cases were detected and randomly matched to 1240 controls at a ratio of 1:1. Those receiving conventional care plus CHM had a substantially lower chance of osteoporosis than those without CHM. Prolonged use of CHM, especially for one year or more, markedly dwindled sequent osteoporosis risk by 71%. Integrating CHM into standard care may favor the improvement of bone function, but further well-designed randomized controlled trials to investigate the possible mechanism are needed

BETTER POSTURAL CONTROL DURING ACCURATE SHOOTING IN ELITE FEMALE BASKETBALL PLAYERS

The purpose of this study was to evaluate the differences of postural control (PC) during accurate and inaccurate shooting in elite female basketball players. 21 female professional basketball players recruited as subjects. The PC was evaluated by the Accusway as sway radius, velocity, radial and 95% area of the center of pressure (COP) during standard penalty line shooting. The results showed that the COP sway area during accurate shooting was significantly smaller than during inaccurate shooting (74.0 ± 37.9 vs. 110.6 ± 49.1, p < .05). Moreover, no significant differences were found between situations in the COP radius and velocity. This study found that during the accurate shooting, elite female basketball player had better PC which demonstrated that significant smaller COP sway area than inaccurate shooting

Maternal residential proximity to major roadways, birth weight, and placental DNA methylation

Exposure to traffic pollution during fetal development has been associated with reduced fetal growth, and there is evidence to suggest that epigenetic mechanisms in the placenta in the form of variant DNA methylation may be a potential mechanism of this effect