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Adv Genet. 2010;72:141-79. doi: 10.1016/B978-0-12-380862-2.00007-2.

Statistical methods for pathway analysis of genome-wide data for association with complex genetic traits.

Author information

  • Biostatistics and Bioinformatics Unit, MRC Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, Cardiff University School of Medicine, Heath Park, Cardiff, United Kingdom.

Abstract

A number of statistical methods have been developed to test for associations between pathways (collections of genes related biologically) and complex genetic traits. Pathway analysis methods were originally developed for analyzing gene expression data, but recently methods have been developed to perform pathway analysis on genome-wide association study (GWAS) data. The purpose of this review is to give an overview of these methods, enabling the reader to gain an understanding of what pathway analysis involves, and to select the method most suited to their purposes. This review describes the various types of statistical methods for pathway analysis, detailing the strengths and weaknesses of each. Factors influencing the power of pathway analyses, such as gene coverage and choice of pathways to analyze, are discussed, as well as various unresolved statistical issues. Finally, a list of computer programs for performing pathway analysis on genome-wide association data is provided.

Copyright © 2010 Elsevier Inc. All rights reserved.

PMID:
21029852
[PubMed - indexed for MEDLINE]
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