Strategies for Pathway Analysis from GWAS Data

Abstract

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.

Pathway analysis of genome-wide association studies on uric acid concentrations

“…Additional studies are needed to confirm the identities of causal SNPs and of genes underlying the associations between pathways and uric acid levels. Nevertheless, pathway-based approaches are set to play an important role in the identification of novel genes that confer disease susceptibility [4]. The goal of pathway-based approaches is not to replace conventional single-maker analysis but to play a complementary part in identifying novel gene or sets of genes that confer disease susceptibility.…”

“…One of the key challenges posed by GWAS data interpretation is the identification of causal single nucleotide polymorphisms (SNPs) and the provision of evidence and hypotheses regarding the mechanism through which they affect the trait being investigated [4]. Furthermore, the use of new methods to study existing GWAS data may provide additional biological insights and highlight new candidate genes [5].…”

From genes to behavior: placing cognitive models in the context of biological pathways

“…In recent years, studies that strike a middle ground, using so-called pathway approaches, are becoming increasingly popular (Figure 2A and Box 1) (Wang et al, 2007, 2010; Yaspan and Veatch, 2011; Ramanan et al, 2012). A genetic pathway consists of a group of functionally related genes that mediate a particular biological process, e.g., DA functioning (Figure 2B).…”

“…The pathway approach addresses these issues by limiting our search to a set of genes underlying a specific biological process, thereby improving the interpretability of potential results (Wang et al, 2007, 2010; Yaspan and Veatch, 2011; Ramanan et al, 2012). For behavior, there are a number of molecularly defined pathways that are suitable as candidates based on previous anatomical, pharmacological and physiological studies in both humans and animals: neuromodulatory pathways (serotonergic, dopaminergic, noradrenergic, etc.…”

Pathway analysis by randomization incorporating structure—PARIS: an update

“…Pathway analysis algorithms were created to aggregate singlevariant statistical analysis results (Yaspan and Veatch, 2011) to identify pathways with enrichment of genetic associations. These build a higher level abstraction of single-variant data and collapse it into biologically informed gene sets comprising pathways.…”