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Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios. [ABSTRACT FROM AUTHOR]

We examined the constitutional provisions on the right to health in the Western Pacific region countries and compared universal health coverage (UHC) achievement. In 9 of the 11 countries, the constitution had provisions related to health rights, of which 7 countries also included details related to the health care system. Additionally, 5 countries also had provisions for the vulnerable. The countries with weak state obligation and no clear provisions on health rights (China and Laos) all recorded low UHC achievement scores. Australia and Malaysia, which do not have constitutional provisions regarding health, have achieved high UHC achievement scores. Constitution is the supreme law of a country and the basis for developing and implementing health and medical laws and policies. In addition, laws or constitutions that regulate the rights to health can help gain access to health care. Follow-up research related to the constitutional right to health will be needed.

Motivation: To address 'missing heritability' issue, many statistical methods for pathway-based analyses using rare variants have been proposed to analyze pathways individually. However, neglecting correlations between multiple pathways can result in misleading solutions, and pathway-based analyses of large-scale genetic datasets require massive computational burden. We propose a Pathway-based approach using HierArchical components of collapsed RAre variants Of High-throughput sequencing data (PHARAOH) for the analysis of rare variants by constructing a single hierarchical model that consists of collapsed gene-level summaries and pathways and analyzes entire pathways simultaneously by imposing ridge-type penalties on both gene and pathway coefficient estimates; hence our method considers the correlation of pathways without constraint by a multiple testing problem., Results: Through simulation studies, the proposed method was shown to have higher statistical power than the existing pathway-based methods. In addition, our method was applied to the large-scale whole-exome sequencing data with levels of a liver enzyme using two well-known pathway databases Biocarta and KEGG. This application demonstrated that our method not only identified associated pathways but also successfully detected biologically plausible pathways for a phenotype of interest. These findings were successfully replicated by an independent large-scale exome chip study., Availability and Implementation: An implementation of PHARAOH is available at http://statgen.snu.ac.kr/software/pharaoh/, Contact: tspark@stats.snu.ac.kr, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)