Dataset Information

Multi-SNP analysis of GWAS data identifies pathways associated with nonalcoholic fatty liver disease.

ABSTRACT: Non-alcoholic fatty liver disease (NAFLD) is a common liver disease; the histological spectrum of which ranges from steatosis to steatohepatitis. Nonalcoholic steatohepatitis (NASH) often leads to cirrhosis and development of hepatocellular carcinoma. To better understand pathogenesis of NAFLD, we performed the pathway of distinction analysis (PoDA) on a genome-wide association study dataset of 250 non-Hispanic white female adult patients with NAFLD, who were enrolled in the NASH Clinical Research Network (CRN) Database Study, to investigate whether biologic process variation measured through genomic variation of genes within these pathways was related to the development of steatohepatitis or cirrhosis. Pathways such as Recycling of eIF2:GDP, biosynthesis of steroids, Terpenoid biosynthesis and Cholesterol biosynthesis were found to be significantly associated with NASH. SNP variants in Terpenoid synthesis, Cholesterol biosynthesis and biosynthesis of steroids were associated with lobular inflammation and cytologic ballooning while those in Terpenoid synthesis were also associated with fibrosis and cirrhosis. These were also related to the NAFLD activity score (NAS) which is derived from the histological severity of steatosis, inflammation and ballooning degeneration. Eukaryotic protein translation and recycling of eIF2:GDP related SNP variants were associated with ballooning, steatohepatitis and cirrhosis. Il2 signaling events mediated by PI3K, Mitotic metaphase/anaphase transition, and Prostanoid ligand receptors were also significantly associated with cirrhosis. Taken together, the results provide evidence for additional ways, beyond the effects of single SNPs, by which genetic factors might contribute to the susceptibility to develop a particular phenotype of NAFLD and then progress to cirrhosis. Further studies are warranted to explain potential important genetic roles of these biological processes in NAFLD.

SUBMITTER: Chen QR

PROVIDER: S-EPMC3716806 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Multi-SNP analysis of GWAS data identifies pathways associated with nonalcoholic fatty liver disease.

Chen Qing-Rong QR Braun Rosemary R Hu Ying Y Yan Chunhua C Brunt Elizabeth M EM Meerzaman Daoud D Sanyal Arun J AJ Buetow Kenneth K

PloS one 20130719 7

Non-alcoholic fatty liver disease (NAFLD) is a common liver disease; the histological spectrum of which ranges from steatosis to steatohepatitis. Nonalcoholic steatohepatitis (NASH) often leads to cirrhosis and development of hepatocellular carcinoma. To better understand pathogenesis of NAFLD, we performed the pathway of distinction analysis (PoDA) on a genome-wide association study dataset of 250 non-Hispanic white female adult patients with NAFLD, who were enrolled in the NASH Clinical Resear ...[more]

PMID: 23894275

Similar Datasets

Project description:Nonalcoholic fatty liver disease (NAFLD) represents a burgeoning worldwide epidemic whose etiology reflects multiple interactions between environmental and genetic factors. Here, we review the major pathways and dominant genetic modifiers known to be relevant players in human NAFLD and which may determine key components of the heritability of distinctive disease traits including steatosis and fibrosis. In addition, we have employed general assumptions which are based on known genetic factors in NAFLD to build a systems biology prediction model that includes functional enrichment. This prediction model highlights additional complementary pathways that represent plausible intersecting signaling networks that we define here as an NAFLD-Reactome. We review the evidence connecting variants in each of the major known genetic modifiers (variants in patatin-like phospholipase domain containing 3, transmembrane 6 superfamily member 2, membrane-bound O-acyltransferase domain containing 7, glucokinase regulator, and hydroxysteroid 17-beta dehydrogenase 13) to NAFLD and expand the associated underlying mechanisms using functional enrichment predictions, based on both preclinical and cell-based experimental findings. These major candidate gene variants function in distinct pathways, including substrate delivery for de novo lipogenesis; mitochondrial energy use; lipid droplet assembly, lipolytic catabolism, and fatty acid compartmentalization; and very low-density lipoprotein assembly and secretion. The NAFLD-Reactome model expands these pathways and allows for hypothesis testing, as well as serving as a discovery platform for druggable targets across multiple pathways that promote NAFLD development and influence several progressive outcomes. In conclusion, we summarize the strengths and weaknesses of studies implicating selected variants in the pathophysiology of NAFLD and highlight opportunities for future clinical research and pharmacologic intervention, as well as the implications for clinical practice.

Project description:Testing for genetic association with multiple traits has become increasingly important, not only because of its potential to boost statistical power, but also for its direct relevance to applications. For example, there is accumulating evidence showing that some complex neurodegenerative and psychiatric diseases like Alzheimer's disease are due to disrupted brain networks, for which it would be natural to identify genetic variants associated with a disrupted brain network, represented as a set of multiple traits, one for each of multiple brain regions of interest. In spite of its promise, testing for multivariate trait associations is challenging: if not appropriately used, its power can be much lower than testing on each univariate trait separately (with a proper control for multiple testing). Furthermore, differing from most existing methods for single-SNP-multiple-trait associations, we consider SNP set-based association testing to decipher complicated joint effects of multiple SNPs on multiple traits. Because the power of a test critically depends on several unknown factors such as the proportions of associated SNPs and of traits, we propose a highly adaptive test at both the SNP and trait levels, giving higher weights to those likely associated SNPs and traits, to yield high power across a wide spectrum of situations. We illuminate relationships among the proposed and some existing tests, showing that the proposed test covers several existing tests as special cases. We compare the performance of the new test with that of several existing tests, using both simulated and real data. The methods were applied to structural magnetic resonance imaging data drawn from the Alzheimer's Disease Neuroimaging Initiative to identify genes associated with gray matter atrophy in the human brain default mode network (DMN). For genome-wide association studies (GWAS), genes AMOTL1 on chromosome 11 and APOE on chromosome 19 were discovered by the new test to be significantly associated with the DMN. Notably, gene AMOTL1 was not detected by single SNP-based analyses. To our knowledge, AMOTL1 has not been highlighted in other Alzheimer's disease studies before, although it was indicated to be related to cognitive impairment. The proposed method is also applicable to rare variants in sequencing data and can be extended to pathway analysis.

Dataset Information

Multi-SNP analysis of GWAS data identifies pathways associated with nonalcoholic fatty liver disease.

Publications

Multi-SNP analysis of GWAS data identifies pathways associated with nonalcoholic fatty liver disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets