Gene set analysis of SNP data: benefits, challenges, and future directions

Abstract

The last decade of human genetic research witnessed the completion of hundreds of genome-wide association studies (GWASs). However, the genetic variants discovered through these efforts account for only a small proportion of the heritability of complex traits. One explanation for the missing heritability is that the common analysis approach, assessing the effect of each singlenucleotide polymorphism (SNP) individually, is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) is one of several approaches that may contribute to the discovery of additional genetic risk factors for complex traits. Complex phenotypes are thought to be controlled by networks of interacting biochemical and physiological pathways influenced by the products of sets of genes. By assessing the overall evidence of association of a phenotype with all measured variation in a set of genes, GSA may identify functionally relevant sets of genes corresponding to relevant biomolecular pathways, which will enable more focused studies of genetic risk factors. This approach may thus contribute to the discovery of genetic variants responsible for some of the missing heritability. With the increased use of these approaches for the secondary analysis of data from GWAS, it is important to understand the different GSA methods and their strengths and weaknesses, and consider challenges inherent in these types of analyses. This paper provides an overview of GSA, highlighting the key challenges, potential solutions, and directions for ongoing research.

Identification of possible pathogenic pathways in Behçet’s disease using genome-wide association study data from two different populations

“…5 Standard GWAS approaches focus on the analysis of single SNPs; however, for multifactorial diseases, no particular variant on a particular gene may have a strong effect, but the combination of multiple variants with small effects explains the overall susceptibility to the disease. 6,7 The disruption of different biological pathways is thought to determine the intrinsic biological processes of multifactorial diseases. In this regard, pathway-based approaches to GWAS may be more helpful in a search for multiple genes involved in the same biological pathway, where the common variations in each of these genes have little correlation with disease risk.…”

Genome-wide pathway analysis in glioma

“…If a specific pathway was relevant to disease susceptibility, association signals would be expected to be overrepresented for SNPs in that pathway [4,[19][20][21]. Given the limited power of GWAS to detect single SNP associations, we adopted a pathway-based approach to take into account the biological interplay between genes and to provide insight into how multiple genes might contribute to the pathogenesis of glioma [22][23][24].…”

Covariance Association Test (CVAT) Identifies Genetic Markers Associated with Schizophrenia in Functionally Associated Biological Processes

“…Therefore, we argue that methods that exploit the inherent characteristics of complex traits, i.e., many genetic markers with small effect sizes scattered around the genome in a nonrandom fashion, will have increased power to detect sets of markers enriched for causal variants. These methods are commonly known as set tests, and a variety of methods have been developed (e.g., Fridley and Biernacka 2011;Mooney et al 2014). …”

“…relatively largest effect (Hirschhorn and Daly 2005;McCarthy et al 2008;Fridley and Biernacka 2011;Wang et al 2011).…”

“…Summary statistics can be obtained in several ways (Fridley and Biernacka 2011;Mooney et al 2014). Commonly, single-marker test statistics are computed for all markers.…”