Genome-wide gene and pathway analysis

Abstract

Current GWAS have primarily focused on testing association of single SNPs. To only test for association of single SNPs has limited utility and is insufficient to dissect the complex genetic structure of many common diseases. To meet conceptual and technical challenges raised by GWAS, we propose gene and pathway-based GWAS as complementary to the current single SNP-based GWAS. This publication develops three statistics for testing association of genes and pathways with disease: linear combination test, quadratic test and decorrelation test which take correlations among SNPs within a gene or genes within a pathway into account. The null distribution of the proposed statistics is examined and the statistics are applied to GWAS of rheumatoid arthritis in the Wellcome Trust Case Control Consortium and the North American Rheumatoid Arthritis Consortium studies. The preliminary results show that the proposed gene and pathway-based GWAS offer several remarkable features. First, not only can they identify the genes that have large genetic effects, but also they can detect new genes in which each single SNP conferred a small amount of disease risk, and their joint actions can be implicated in the development of diseases. Second, gene and pathway-based analysis can allow the formation of the core of pathway definition of complex diseases and unravel the functional bases of an association finding. Third, replication of association findings at the gene or pathway level is much easier than replication at the individual SNP level.

Unraveling Genetic Origin of Aging-Related Traits: Evolving Concepts

“…One of them is to use biological evidence for validation. 108,109 Another strategy could be to use pleiotropy. The latter is plausible because evidence on additive associations of the same genetic variant with different traits reduces the probability of false discoveries of stochastic origin, especially, in framework of systemic strategies.…”

Pathway analysis with next-generation sequencing data

“…2 Pathway analysis typically tests the association of a predefined set of related genes, which are often defined by biological knowledge. 3 Although pathway analysis methods have been developed and successfully applied to association studies of common variants, [4][5][6][7][8][9][10][11][12][13][14][15][16][17] the statistical methods for pathway-based association analysis of rare variants have not been well developed. [18][19][20][21] The current methods for pathway-based association analysis of rare variants are classified into two approaches.…”

“…To evaluate the performance of the SFPCA-based statistic for pathway analysis, we will use large-scale simulations to calculate the type I error rates and systematically evaluate the power of 23 statistical methods: SFPCA, functional principal component analysis (FPCA), the weighted sum (WSS), 24 variable-threshold (VT), 25 combined multivariate and collapsing (CMC), 26 linear combination test (LCT/LCT), 13 quadratic test (QT/QT), 13 de-correlation test (DT/DT), 13 WSS/Sidak, WSS/Fisher combination, WSS/Fisher exact, WSS/GESA, VT/Sidak, VT/Fisher combination, VT/Fisher exact, VT/GESA, CMC/Sidak, CMC/Fisher combination, CMC/Fisher exact, CMC/GESA, PCA, SKAT 27 and GESA. 28 To further explore and illustrate some valuable features of the SFPCA, SFPCA and other popular statistics for pathway analysis are applied to the early-onset myocardial infarction (EOMI) exome sequence data sets, which contain individuals with European origin (EA) and African origin (AA) from the NHLBI's Exome Sequencing Project (ESP).…”

Genotyping in rheumatoid arthritis: a game changer in clinical management?

“…However, gratifying such corroborative data may be in terms of pathophysiological understanding, genetic studies have not, to date, helped to identify novel therapeutic targets. However, recent studies focusing on biologically relevant pathways rather than individual genetic loci have provided some encouraging insights [10,11]. In a groundbreaking investigation, Okada et al integrated publicly available drug target-gene data with findings of a novel GWAS meta-analysis [4].…”