A pathway analysis method for genome-wide association studies.

Abstract:

:For genome-wide association studies, we propose a new method for identifying significant biological pathways. In this approach, we aggregate data across single-nucleotide polymorphisms to obtain summary measures at the gene level. We then use a hierarchical Bayesian model, which takes the gene-level summary measures as data, in order to evaluate the relevance of each pathway to an outcome of interest (e.g., disease status). Although shifting the focus of analysis from individual genes to pathways has proven to improve the statistical power and provide more robust results, such methods tend to eliminate a large number of genes whose pathways are unknown. For these genes, we propose to use a Bayesian multinomial logit model to predict the associated pathways by using the genes with known pathways as the training data. Our hierarchical Bayesian model takes the uncertainty regarding the pathway predictions into account while assessing the significance of pathways. We apply our method to two independent studies on type 2 diabetes and show that the overlap between the results from the two studies is statistically significant. We also evaluate our approach on the basis of simulated data.

journal_name

Stat Med

journal_title

Statistics in medicine

authors

Shahbaba B,Shachaf CM,Yu Z

doi

10.1002/sim.4477

subject

Has Abstract

pub_date

2012-05-10 00:00:00

pages

988-1000

issue

10

eissn

0277-6715

issn

1097-0258

journal_volume

31

pub_type

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