Two novel pathway analysis methods based on a hierarchical model.


Evangelou, M; Dudbridge, F; Wernisch, L; (2013) Two novel pathway analysis methods based on a hierarchical model. Bioinformatics (Oxford, England). ISSN 1367-4803 DOI: https://doi.org/10.1093/bioinformatics/btt583

[img]
Preview
Text - Published Version
License:

Download (216kB) | Preview

Abstract

MOTIVATION Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date. METHODS We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways. RESULTS The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher's method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved.Availability: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors.Contact: marina.evangelou@cimr.cam.ac.ukSupplementary Information: Supplementary data are available at Bioinformatics online.

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
Research Centre: Centre for Statistical Methodology
PubMed ID: 24123673
Web of Science ID: 332259300014
URI: http://researchonline.lshtm.ac.uk/id/eprint/1343320

Statistics


Download activity - last 12 months
Downloads since deposit
250Downloads
293Hits
Accesses by country - last 12 months
Accesses by referrer - last 12 months
Impact and interest
Additional statistics for this record are available via IRStats2

Actions (login required)

Edit Item Edit Item