Heart failure and late-onset Alzheimer's disease: A Mendelian randomization study

Some observational studies suggested that heart failure (HF) is associated with increased risk of late-onset Alzheimer's disease (AD). On the other hand, a recently published Two-Sample Mendelian Randomization (2SMR) study was reported as inconclusive but the estimated odds ratios (ORs) were less than one indicating a potential causal association between genetically predicted HF and lowered risk of AD. Both HF and AD are quite common among elderly persons and frequently occur together resulting in a series of severe medical challenges and increased financial burden on healthcare. It is of great medical and financial interest to further investigate the statistical significance of the potential causal associations between genetically predicted HF and lowered risk of AD using large independent cohorts. To fill this important knowledge gap, the present study used the 2SMR method based on summary data from a recently published large genome-wide association study (GWAS) for AD on subjects with European ancestry. The 2SMR analysis provided statistically significant evidence of an association with ORs less than one between genetically predicted HF and late-onset AD (Inverse Variance Weighted, OR = 0.752, p = 0.004; MR Egger, OR = 0.546, p = 0.100; Weighted Median, OR = 0.757, p = 0.014). Further investigations of the significant associations between HF and late-onset AD, including specific genes related to the potential protective effect of HF-related medications on cognitive decline, are warranted.

Chromosomal Translocations Detection in Cancer Cells Using Chromosomal Conformation Capture Data

Complex chromosomal rearrangements such as translocations play a critical role in oncogenesis. Translocation detection is vital to decipher their biological role in activating cancer-associated mechanisms. High-throughput chromosomal conformations capture (Hi-C) data have shown promising progress in unveiling the genome variations in a disease condition. Until now, multiple structural data (Hi-C)-based methods are available that can detect translocations in cancer genomes. However, the consistency and specificity of Hi-C-based translocation results still need to be validated with conventional methods. This study used Hi-C data of cancerous cell lines, namely lung cancer (A549), Chronic Myelogenous Leukemia (K562), and Acute Monocytic Leukemia (THP-1), to detect the translocations. The results were cross-validated through whole-genome sequencing (WGS) and paired-read analysis. Moreover, PCR amplification validated the presence of translocated reads in different chromosomes. By integrating different data types, we showed that the results of Hi-C data are as reliable as WGS and can be utilized as an assistive method for detecting translocations in the diseased genome. Our findings support the utility of Hi-C technology to detect the translocations and study their effects on the three-dimensional architecture of the genome in cancer condition.

Structural Variations of the 3D Genome Architecture in Cervical Cancer Development

Human papillomavirus (HPV) integration is the major contributor to cervical cancer (CC) development by inducing structural variations (SVs) in the human genome. SVs are directly associated with the three-dimensional (3D) genome structure leading to cancer development. The detection of SVs is not a trivial task, and several genome-wide techniques have greatly helped in the identification of SVs in the cancerous genome. However, in cervical cancer, precise prediction of SVs mainly translocations and their effects on 3D-genome and gene expression still need to be explored. Here, we have used high-throughput chromosome conformation capture (Hi-C) data of cervical cancer to detect the SVs, especially the translocations, and validated it through whole-genome sequencing (WGS) data. We found that the cervical cancer 3D-genome architecture rearranges itself as compared to that in the normal tissue, and 24% of the total genome switches their A/B compartments. Moreover, translocation detection from Hi-C data showed the presence of high-resolution t(4;7) (q13.1; q31.32) and t(1;16) (q21.2; q22.1) translocations, which disrupted the expression of the genes located at and nearby positions. Enrichment analysis suggested that the disrupted genes were mainly involved in controlling cervical cancer-related pathways. In summary, we detect the novel SVs through Hi-C data and unfold the association among genome-reorganization, translocations, and gene expression regulation. The results help understand the underlying pathogenicity mechanism of SVs in cervical cancer development and identify the targeted therapeutics against cervical cancer.

ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments

Chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) is an important experimental method for detecting specific protein-mediated chromatin loops genome-wide at high resolution. Here, we proposed a new statistical approach with a mixture model, chromatin interaction analysis using mixture model (ChIAMM), to detect significant chromatin interactions from ChIA-PET data. The statistical model is cast into a Bayesian framework to consider more systematic biases: the genomic distance, local enrichment, mappability, and GC content. Using different ChIA-PET datasets, we evaluated the performance of ChIAMM and compared it with the existing methods, including ChIA-PET Tool, ChiaSig, Mango, ChIA-PET2, and ChIAPoP. The result showed that the new approach performed better than most top existing methods in detecting significant chromatin interactions in ChIA-PET experiments.

Risk Factors and Treatment Types for Asthma Severity Among Adult Patients

OBJECTIVE

The aim of this study was to identify the risk factors and treatment types for asthma severity among adult patients by applying a retrospective study design.

MATERIALS AND METHODS

The symptoms of asthma and corresponding medication prescription were addressed by descriptive statistics, and an ordinal logistic regression model was applied to identify the risk factors of asthma severity based on the data obtained from chronic follow-up of 422 adult asthma patients from September 11, 2012, to July 8, 2016, at the University of Gondar Teaching Hospital (UOGTH).

RESULTS

From 422 study units, the more commonly presenting asthma symptoms were coughing and wheezing expressed by 52.13% and 50.9%, respectively. For the treatment type given to the patients, oxygen and prednisolone were highly distributed drugs to the patients in chronic illness, medication and follow-up clinic of the University of Gondar Teaching Hospital (UOGTH) which were expressed by 73.5% and 35.5%, respectively. The proportional odd logit model was used to analyse asthma severity in patients; patients who were female (OR=1.68), a rural resident (OR=1.56), regular physical exercise (OR=2.39), allergen to pet (OR=3.17), had asthma in childhood (OR=2.27), had a family history (OR=1.89), and had depression (OR=2.31) were more likely to increase asthma severity than others, and patients who were in case with regular cooker, dry season was less likely to increase asthma severity.

CONCLUSION

Generally, the study presented the most common asthma symptoms and treatment types correspondingly. The study also showed that demographic, environmental, genetic, and health-related factors have a significant effect on asthma severity.