Dataset Information

Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks.

ABSTRACT: To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis.We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 well-characterized COPD cohorts: COPDGene and GenKOLS. We used a third well-characterized COPD case-control cohort for replication: ECLIPSE. Next, we used dmGWAS, a method that integrates GWAS results with PPI, to identify COPD disease modules.No gene-sets reached experiment-wide significance in either discovery population. We identified a consensus network of 10 genes identified in modules by integrating GWAS results with PPI that replicated in COPDGene, GenKOLS, and ECLIPSE. Members of 4 gene-sets were enriched among these 10 genes: (i) lung adenocarcinoma tumor-sequencing genes, (ii) IL-7 pathway genes, (iii) kidney cell response to arsenic, and (iv) CD4 T-cell responses. Further, several genes have also been associated with pathophysiology relevant to COPD including KCNK3, NEDD4L, and RIN3. In particular, KCNK3 has been associated with pulmonary arterial hypertension, a common complication in advanced COPD.We report a set of new genes that may influence the etiology of COPD that would not have been identified using traditional GWAS and pathway analyses alone.

SUBMITTER: McDonald ML

PROVIDER: S-EPMC4415367 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks.

McDonald Merry-Lynn Noelle ML Mattheisen Manuel M Cho Michael H MH Liu Yang-Yu YY Harshfield Benjamin B Hersh Craig P CP Bakke Per P Gulsvik Amund A Lange Christoph C Beaty Terri H TH Silverman Edwin K EK

Human heredity 20140827 3-4

<h4>Objectives</h4>To use a systems biology approach to integrate genotype and protein-protein interaction (PPI) data to identify disease network modules associated with chronic obstructive pulmonary disease (COPD) and to perform traditional pathway analysis.<h4>Methods</h4>We utilized a standard gene-set association approach (FORGE) using gene-based association analysis and gene-set definitions from the molecular signatures database (MSigDB). As a discovery step, we analyzed GWAS results from 2 ...[more]

PMID: 25171373

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Project description:Purpose of reviewNeurodegenerative diseases, neuropsychiatric disorders, and related traits have highly complex etiologies but are also highly heritable and identifying the causal genes and biological pathways underlying these traits may advance the development of treatments and preventive strategies. While many genome-wide association studies (GWAS) have successfully identified variants contributing to polygenic neurodegenerative and neuropsychiatric phenotypes including Alzheimer's disease (AD), schizophrenia (SCZ), and bipolar disorder (BPD) amongst others, interpreting the biological roles of significantly-associated variants in the genetic architecture of these traits remains a significant challenge. Here we review several 'omics' approaches which attempt to bridge the gap from associated genetic variants to phenotype by helping define the functional roles of GWAS loci in the development of neuropsychiatric disorders and traits.Recent findingsSeveral common 'omics' approaches have been applied to examine neuropsychiatric traits, such as nearest-gene mapping, trans-ethnic fine mapping, annotation enrichment analysis, transcriptomic analysis, and pathway analysis, and each of these approaches has strengths and limitations in providing insight into biological mechanisms. One popular emerging method is the examination of tissue-specific genetically-regulated gene expression (GReX), which aggregates the genetic variants' effects at the gene-level. Furthermore, proteomic, metabolomic, and microbiomic studies and phenome-wide association studies will further enhance our understanding of neuropsychiatric traits.SummaryGWAS has been applied to neuropsychiatric traits for a decade, but our understanding about the biological function of identified variants remains limited. Today, technological advancements have created analytical approaches for integrating transcriptomics, metabolomics, proteomics, pharmacology and toxicology as tools for understanding the functional roles of genetics variants. These data, as well as the broader clinical information provided by electronic health records, can provide additional insight and complement genomic analyses.

Project description:BackgroundIdentifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients.ResultsIn this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations.ConclusionsThe analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.

Dataset Information

Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks.

Publications

Beyond GWAS in COPD: probing the landscape between gene-set associations, genome-wide associations and protein-protein interaction networks.

Similar Datasets

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