UNIT 1.20 Strategies for Pathway Analysis from GWAS Data

  1. Brian L. Yaspan,
  2. Olivia J. Veatch

Published Online: 1 OCT 2011

DOI: 10.1002/0471142905.hg0120s71

Current Protocols in Human Genetics

Current Protocols in Human Genetics

How to Cite

Yaspan, B. L. and Veatch, O. J. 2011. Strategies for Pathway Analysis from GWAS Data. Current Protocols in Human Genetics. 71:1.20:1.20.1–1.20.15.

Author Information

  1. Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee

Publication History

  1. Published Online: 1 OCT 2011
  2. Published Print: OCT 2011


Genome-wide association studies (GWAS) are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p values are declared statistically significant due to issues arising from multiple testing and type I error concerns. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, as they are difficult to separate from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. Curr. Protoc. Hum. Genet. 71:1.20.1-1.20.15 © 2011 by John Wiley & Sons, Inc.


  • GWAS;
  • genome-wide association;
  • pathway analysis;
  • genetic epidemiology