Brain APOE expression quantitative trait loci-based association study identified one susceptibility locus for Alzheimer\u27s disease by interacting with APOE epsilon 4

AbstractSome studies have demonstrated interactions of AD-risk single nucleotide polymorphisms (SNPs) in non-APOE regions with APOE genotype. Nevertheless, no study reported interactions of expression quantitative trait locus (eQTL) for APOE with APOE genotype. In present study, we included 9286 unrelated AD patients and 8479 normal controls from 12 cohorts of NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). 34 unrelated brain eQTLs for APOE were compiled from BRAINEAC and GTEx. We used multi-covariate logistic regression analysis to identify eQTLs interacted with APOE ε4. Adjusted for age and gender, substantia nigra eQTL rs438811 for APOE showed significantly strong interaction with APOE ε4 status (OR, 1.448; CI, 1.124–1.430; P-value = 7.94 × 10−6). APOE ε4-based sub-group analyses revealed that carrying one minor allele T of rs438811 can increase the opportunity of developing to AD by 26.75% in APOE ε4 carriers but not in non-carriers. We revealed substantia nigra eQTL rs438811 for APOE can interact with APOE ε4 and confers risk in APOE ε4 carriers only.</jats:p

Dysbiosis of vaginal and cervical microbiome is associated with uterine fibroids

Dysbiosis of the female reproductive tract is closely associated with gynecologic diseases. Here, we aim to explore the association between dysbiosis in the genital tract and uterine fibroids (UFs) to further provide new insights into UF etiology. We present an observational study to profile vaginal and cervical microbiome from 29 women with UFs and 38 healthy women, and 125 samples were obtained and sequenced. By comparing the microbial profiles between different parts of the reproductive tract, there is no significant difference in microbial diversity between healthy subjects and UF patients. However, alpha diversity of UF patients was negatively correlated with the number of fibroids. Increased Firmicutes were observed in both the cervical and vaginal microbiome of UF patients at the phylum level. In differential analysis of relative abundance, some genera were shown to be significantly enriched (e.g., Erysipelatoclostridium, Mucispirillum, and Finegoldia) and depleted (e.g., Erysipelotrichaceae UCG-003 and Sporolactobacillus) in UF patients. Furthermore, the microbial co-occurrence networks of UF patients showed lower connectivity and complexity, suggesting reduced interactions and stability of the cervical and vaginal microbiota in UF patients. In summary, our findings revealed the perturbation of microbiome in the presence of UFs and a distinct pattern of characteristic vaginal and cervical microbiome involved in UFs, offering new options to further improve prevention and management strategies

Variable selection for large p small n regression models with incomplete data: Mapping QTL with epistases

<p>Abstract</p> <p>Background</p> <p>Identifying quantitative trait loci (QTL) for both additive and epistatic effects raises the statistical issue of selecting variables from a large number of candidates using a small number of observations. Missing trait and/or marker values prevent one from directly applying the classical model selection criteria such as Akaike's information criterion (AIC) and Bayesian information criterion (BIC).</p> <p>Results</p> <p>We propose a two-step Bayesian variable selection method which deals with the sparse parameter space and the small sample size issues. The regression coefficient priors are flexible enough to incorporate the characteristic of "large <it>p </it>small <it>n</it>" data. Specifically, sparseness and possible asymmetry of the significant coefficients are dealt with by developing a Gibbs sampling algorithm to stochastically search through low-dimensional subspaces for significant variables. The superior performance of the approach is demonstrated via simulation study. We also applied it to real QTL mapping datasets.</p> <p>Conclusion</p> <p>The two-step procedure coupled with Bayesian classification offers flexibility in modeling "large p small n" data, especially for the sparse and asymmetric parameter space. This approach can be extended to other settings characterized by high dimension and low sample size.</p

Long-term follow-up outcome and reintervention analysis of ultrasound-guided high intensity focused ultrasound treatment for uterine fibroids

Objective To investigate the long-term reintervention of ultrasound-guided high intensity focused ultrasound (USgHIFU) treatment for uterine fibroids and analyze the factors affecting reintervention rate after USgHIFU. Materials and methods Three hundred and eight-one patients with uterine fibroids treated by USgHIFU at the third Xiangya Hospital of Central South University from April 2012 to December 2014 were retrospectively reviewed. The factors that affect the reintervention rate were analyzed. Results The mean follow-up time was 70.0 ± 9.0 months. During the follow-up period, 86.4% (329/381) of the patients reported symptomatic relief and the fibroids shrank after USgHIFU treatment. Seventy-nine patients received reintervention included myomectomy, a second session of HIFU, and hysterectomy. The overall reintervention rate was 20.7% (79/381). The reasons for reintervention included symptomatic recurrence in 50 (50/79, 63.3%) patients, psychological factors in 14 (14/79, 17.7%) patients, fertility requirement in three (3/79, 3.8%) patients, suspected uterine sarcoma in two (2/79, 2.5%) patients and others in 10 (10/79, 12.7%) patients. The reintervention rate has significant correlation with some factors including age, size, type and the signal intensity on T2 weighted image (T2WI) of the uterine fibroids. Conclusion USgHIFU for uterine fibroids is effective due to low reintervention rate in a long-term follow-up

HYPOTHESIS Open Access Management of long-term and reversible

hysteroscopic sterilization: a novel device with nickel-titanium shape memory allo

Candida albicans-induced activation of the TGF-β/Smad pathway and upregulation of IL-6 may contribute to intrauterine adhesion

Abstract Iatrogenic injury to endometrial tissue is the main cause of intrauterine adhesions (IUA) and infection can also damage the endometrium. The microbiota plays an important role in the health of the female reproductive tract. However, the mechanism is still unclear. In total, 908 patients with IUA and 11,389 healthy individuals were retrospectively selected for this clinical study. Participant information including vaginal microecological results and human papillomavirus (HPV) status were collected. Univariate and multivariate logistic regression analyses were used to identify the factors related to IUA. Next, animal experiments were performed in a curettage-induced IUA rat model. After the procedure, rats in the experimental group received a vaginal infusion of a Candida albicans (C. albicans) fungal solution. On days 3, 7, and 14 after curettage and infusion, the expression levels of IL-6, fibrotic pathway-related factors (TGF-β1, Smad 2, and COL1), and estrogen receptor (ER) and progesterone receptor (PR) in rat endometrial tissues were assessed. Fungal infection of the reproductive tract was found to be an independent risk factor for IUA (P < 0.05). The inflammatory response and degree of fibrosis were greater in rats infected with C. albicans than in the controls. The levels of IL-6, TGF-β1, Smad 2, and COL1 expression in endometrial tissues were significantly higher in the experimental group than in the control group (P < 0.05). However, the ER and PR levels were lower in the IUA group than in the non-IUA group (P < 0.05). C. albicans infection may be related to IUA. C. albicans elicits a strong inflammatory response that can lead to more severe endometrial fibrosis

SIGNET: transcriptome-wide causal inference for gene regulatory networks

Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available ( https://www.zstats.org/signet/ )