Analysis of untyped SNPs: maximum likelihood and imputation methods

Analysis of untyped single nucleotide polymorphisms (SNPs) can facilitate the localization of disease-causing variants and permit meta-analysis of association studies with different genotyping platforms. We present two approaches for using the linkage disequilibrium structure of an external reference panel to infer the unknown value of an untyped SNP from the observed genotypes of typed SNPs. The maximum-likelihood approach integrates the prediction of untyped genotypes and estimation of association parameters into a single framework and yields consistent and efficient estimators of genetic effects and gene-environment interactions with proper variance estimators. The imputation approach is a two-stage strategy, which first imputes the untyped genotypes by either the most likely genotypes or the expected genotype counts and then uses the imputed values in a downstream association analysis. The latter approach has proper control of type I error in single-SNP tests with possible covariate adjustments even when the reference panel is misspecified; however, type I error may not be properly controlled in testing multiple-SNP effects or gene-environment interactions. In general, imputation yields biased estimators of genetic effects and gene-environment interactions, and the variances are underestimated. We conduct extensive simulation studies to compare the bias, type I error, power, and confidence interval coverage between the maximum likelihood and imputation approaches in the analysis of single-SNP effects, multiple-SNP effects, and gene-environment interactions under cross-sectional and case-control designs. In addition, we provide an illustration with genome-wide data from the Wellcome Trust Case-Control Consortium (WTCCC) [2007]

Two charged strangeonium-like structures observable in the Y(2175)→ϕ(1020)π+π−Y(2175) \to \phi(1020)\pi^{+} \pi^{-} process

Via the Initial Single Pion Emission (ISPE) mechanism, we study the $\phi(1020)\pi^{+}$ invariant mass spectrum distribution of $Y(2175) \to \phi(1020)\pi^{+} \pi^{-}$. Our calculation indicates there exist a sharp peak structure ($Z_{s1}^+$) close to the $K\bar{K}^\ast$ threshold and a broad structure ($Z_{s2}^+$) near the $K^\ast\bar{K}^\ast$ threshold. In addition, we also investigate the $\phi(1680) \to \phi(1020)\pi^{+} \pi^{-}$ process due to the ISPE mechanism, where a sharp peak around the $K\bar{K}^\ast$ threshold appears in the $\phi(1020)\pi^{+}$ invariant mass spectrum distribution. We suggest to carry out the search for these charged strangeonium-like structures in future experiment, especially Belle II, Super-B and BESIII.Comment: 7 pages, 5 figures. Accepted by Eur. Phys. J.

Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses

The pharmaceutical industry and regulatory agencies are increasingly interested in conducting bridging studies in order to bring an approved drug product from the original region (eg, United States or European Union) to a new region (eg, Asian-Pacific countries). In this article, we provide a new methodology for the design and analysis of bridging studies by assuming prior knowledge on how the null and alternative hypotheses in the original, foreign study are related to the null and alternative hypotheses in the bridging study and setting the type I error for the bridging study according to the strength of the foreign-study evidence. The new methodology accounts for randomness in the foreign-study evidence and controls the average type I error of the bridging study over all possibilities of the foreign-study evidence. In addition, the new methodology increases statistical power, when compared to approaches that do not use foreign-study evidence, and it allows for the possibility of not conducting the bridging study when the foreign-study evidence is unfavorable. Finally, we conducted extensive simulation studies to demonstrate the usefulness of the proposed methodology

Efficient Estimation for Semiparametric Structural Equation Models With Censored Data

Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined expectation-maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, we demonstrate the satisfactory performance of the proposed methods through simulation studies and provide an application to a motivating cancer study that contains a variety of genomic variables. Supplementary materials for this article are available online. © 2018, © 2018 American Statistical Association

Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data

Interval-censored multivariate failure time data arise when there are multiple types of failure or there is clustering of study subjects and each failure time is known only to lie in a certain interval.We investigate the effects of possibly time-dependent covariates on multivariate failure times by considering a broad class of semiparametric transformation models with random effects, and we study nonparametric maximum likelihood estimation under general interval-censoring schemes.We show that the proposed estimators for the finite-dimensional parameters are consistent and asymptotically normal, with a limiting covariance matrix that attains the semiparametric efficiency bound and can be consistently estimated through profile likelihood. In addition, we develop an EM algorithm that converges stably for arbitrary datasets. Finally, we assess the performance of the proposed methods in extensive simulation studies and illustrate their application using data derived from the Atherosclerosis Risk in Communities Study

Compact graphene mode-locked wavelength-tunable erbium-doped fiber lasers: from all anomalous dispersion towards all normal dispersion

Soliton operation and soliton wavelength tuning of erbium-doped fiber lasers mode locked with atomic layer graphene was experimentally investigated under various cavity dispersion conditions. It was shown that not only wide range soliton wavelength tuning but also soltion pulse width variation could be obtained in the fiber lasers. Our results show that the graphene mode locked erbium-doped fiber lasers provide a compact, user friendly and low cost wavelength tunable ultrahsort pulse source

Increasing levels of circulating Th17 cells and interleukin-17 in rheumatoid arthritis patients with an inadequate response to anti-TNF-α therapy

Introduction: The objective of this study was to investigate the effects of tumor necrosis factor (TNF)-alpha inhibitors on circulating T helper-type 17 (Th17) cells and Th17-related cytokines in patients with rheumatoid arthritis (RA). Methods: The frequencies of circulating Th17 cells and serum levels of Th17-related cytokines were determined using flow cytometry analysis and ELISA, respectively, in 48 RA patients both before (baseline) and six months after anti-TNF-alpha therapy. Therapeutic response was evaluated using European League Against Rheumatism (EULAR) response criteria. Results: Significantly higher baseline frequencies of circulating Th17 cells and serum levels of interleukin (IL)-6, IL17, IL-21, IL-23 and TNF-alpha were observed in active RA patients than in 12 healthy controls (all P < 0.001). After anti-TNF-alpha therapy, 36 patients (75%) were EULAR responders (20 good responders and 16 moderate responders) and 12 (25.0%) were non-responders. The mean levels of circulating Th17 cells and IL-17 significantly decreased (1.13% vs. 0.79%; 43.1 pg/ml vs. 27.8 pg/ml; respectively, both P < 0.001) in parallel with clinical remission in responders. Levels of IL-6, IL-21, IL-23 and TNF-alpha were significantly decreased after anti-TNF-alpha therapy in responders. In contrast, the mean levels of circulating Th17 cells and IL-17 significantly increased after anti-TNF-alpha therapy (2.94% vs. 4.23%; 92.1 pg/ml vs. 148.6 pg/ml; respectively, both P < 0.05) in non-responders. Logistic regression analysis identified a high baseline level of IL-17 as a significant predictor of poor therapeutic response. Conclusions: The beneficial effect of anti-TNF-alpha therapy might involve a decrease in Th17-related cytokines in responders, whereas rising levels of circulating Th17-cells and IL-17 were observed in patients with an inadequate response to anti-TNF-alpha therapy

Why doorstepping can increase household waste recycling

AbstractIn this study we report on a doorstepping intervention which produced a 12.5%, statistically significant, increase in the recycling capture rate. More importantly, we investigate why doorstepping caused the increase, through focus groups, structured interviews and questionnaires. By analyzing the findings with respect to a pragmatic set of eleven clusters of determinants of behaviour change, we find that social norms and emotion were important, with prompts as a more minor determinant. We can now plan further doorstepping knowing an emphasis on these is useful. Knowledge, skills, belief of consequences, belief of capability, action planning, role clarification, feedback, and motivation were determinant clusters found not to be important in this case.Recycling behaviour change interventions often do not generally produce transferable learning because they are usually presented as case studies and not broken down into key elements. Our analytical approach of breaking down a poorly defined activity – doorstepping – into elements which influence different clusters of determinants, and then exploring their separate impacts, allows some predictive planning and optimization for other interventions. The specific context here was residential food waste recycling in apartment blocks of communities in Shanghai, China

Study of the Anti-Proliferative Activity of 5-Substituted 4,7-Dimethoxy-1,3-Benzodioxole Derivatives of SY-1 from Antrodia camphorata on Human COLO 205 Colon Cancer Cells

A set of 10 4,7-dimethoxy-1,3-benzodioxole derivatives based on a lead compound previously discovered by our group, SY-1, which was isolated from Antrodia camphorata, were evaluated for their in vitro inhibitory activity on human colorectal carcinoma cells (COLO 205). Structure-activity relationship studies of the 10 compounds indicated the importance of the chain length of the alkyl group at the 5-position, and the 2-propenyl substituent named “apiole” exhibited the most potent inhibitory activity. In the present study, we demonstrate that the SY-1 analogue “apiole” decreased the proliferation of COLO 205 cells, but not that of normal human colonic epithelial cells (FHC). The G0/G1 cell cycle arrest induced by apiole (75–225 μM) was associated with significantly increased levels of p53, p21 and p27 and decreased levels of cyclin D1. Concerning COLO 205 cell apoptosis, apiole (>150 μM) treatment significantly increased the levels of cleaved caspases 3, 8, 9 and bax/bcl-2 ratio and induced ladder formation in DNA fragmentation assay and sub-G1 peak in flow cytometry analysis. These findings suggest that apiole can suppress COLO 205 cell growth; however, the detailed mechanisms of these processes require further investigation

Semiparametric Regression Analysis of Multiple Right- and Interval-Censored Events

Health sciences research often involves both right- and interval-censored events because the occurrence of a symptomatic disease can only be observed up to the end of follow-up, while the occurrence of an asymptomatic disease can only be detected through periodic examinations. We formulate the effects of potentially time-dependent covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects that capture the dependence both within and between the two types of events. We consider nonparametric maximum likelihood estimation and develop a simple and stable EM algorithm for computation. We show that the resulting estimators are consistent and the parametric components are asymptotically normal and efficient with a covariance matrix that can be consistently estimated by profile likelihood or nonparametric bootstrap. In addition, we leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history. Furthermore, we assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an application to a major epidemiological cohort study. Supplementary materials for this article are available online