Pathway analysis of complex diseases for GWAS, extending to consider rare variants, multi-omics and interactions

Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other “-omics” and interaction data. Scope of review 1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other “-omics” and interaction data. Major conclusions To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other “-omics” data and interaction can better explain gene functions. General significance Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.