Dataset Information

Pathway analysis by randomization incorporating structure-PARIS: an update.

ABSTRACT:

Motivation

We present an update to the pathway enrichment analysis tool 'Pathway Analysis by Randomization Incorporating Structure (PARIS)' that determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating many of the over-testing concerns arising with other pathway analysis approaches.

Results

We have updated PARIS to incorporate expanded pathway definitions through the incorporation of new expert knowledge from multiple database sources, through customized user provided pathways, and other improvements in user flexibility and functionality.

Availability and implementation

PARIS is freely available to all users at https://ritchielab.psu.edu/software/paris-download

Contact

jnc43@case.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Butkiewicz M

PROVIDER: S-EPMC4965631 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Pathway analysis by randomization incorporating structure-PARIS: an update.

Butkiewicz Mariusz M Cooke Bailey Jessica N JN Frase Alex A Dudek Scott S Yaspan Brian L BL Ritchie Marylyn D MD Pendergrass Sarah A SA Haines Jonathan L JL

Bioinformatics (Oxford, England) 20160307 15

<h4>Motivation</h4>We present an update to the pathway enrichment analysis tool 'Pathway Analysis by Randomization Incorporating Structure (PARIS)' that determines aggregated association signals generated from genome-wide association study results. Pathway-based analyses highlight biological pathways associated with phenotypes. PARIS uses a unique permutation strategy to evaluate the genomic structure of interrogated pathways, through permutation testing of genomic features, thus eliminating man ...[more]

PMID: 27153576

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Project description:Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bi-directional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring causal relationships in complex data sets such as those generated from modern "omics" technologies.

Dataset Information

Pathway analysis by randomization incorporating structure-PARIS: an update.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Pathway analysis by randomization incorporating structure-PARIS: an update.

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