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

https://doi.org/10.1016/j.bbagen.2016.11.030Get rights and content
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Highlights

  • Pathway analysis (PA) combines variant effects in genome-wide association studies.

  • Many factors are considered when choosing appropriate software for PA.

  • PA can also be used for other data: rare variants, other -omics & interaction data.

  • PA can be expanded to other data types to improve disease outcome prediction.

Abstract

Background

Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. 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.

Abbreviations

ARTP
adaptive rank truncated product
BMI
body mass index
(r)BEE
(robust) brain-expressed enhancer
CNV
copy number variation
eQTL
expression quantitative trait locus
ESI-MS/MS
electrospray ionization mass spectrometry
eSNP
expression single-nucleotide polymorphism
GCN
gene coexpression network
G-E
gene-environment
G-G
gene-gene
GEWIS
genome-wide interaction study
GO
gene ontology
GRN
gene regulatory network
GSAA
gene set association analysis
GSEA
gene set enrichment analysis
GWAS
genome-wide association study
HLA
human leukocyte antigen
iGSEA
improved gene set enrichment analysis
iGWAS
integrative GWAS
IPA
Ingenuity Pathway Analysis
KEGG
Kyoto Encyclopaedia of Genes and Genomes
lincRNA
long intervening non-coding RNA
LD
linkage disequilibrium
MAF
minor allele frequency
mGWAS
GWAS on metabolic traits
MIAME
minimum information about a microarray experiment
MS
mass spectrometry
NGS
next-generation sequencing
NMR
nuclear magnetic resonance
PPI
protein-protein interaction
RV
rare variant
SKAT
sequence kernel association test
SNP
single-nucleotide polymorphism
WGCNA
weighted gene coexpression network analysis
WKS
weighted Kolmogorov–Smirnov (statistics)

Keywords

Pathway analysis
Genome-wide association study (GWAS)
Complex disease
Multi-omics
Interaction
Rare variants

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