Using the gene ontology to scan multilevel gene sets for associations in genome wide association studies

Abstract

Gene-set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene-set, have plagued current approaches, often leading to ad hoc “fixes”. To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene-level and gene-set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene-sets. However, other types of gene-set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene-sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene-sets, we use an efficient computational strategy that accounts for LD and provides accurate step-down adjusted p-values for each gene-set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene-set analyses.

A Powerful Pathway-Based Adaptive Test for Genetic Association with Common or Rare Variants

“…Perhaps due to the adaptivity of the original aSPUpath test and possible loss of information by PCs, we did not find improvement by the use of PCs in our simulations. However, given that PCbased tests 27,28 are viable competitors to variance-component tests as discussed in Schaid et al, 12 we had an interesting, perhaps surprising, observation: applying the SPU(2) (i.e., SSU) test (that is equivalent to a variance-component test) to the original genotypes or the PCs gave almost the same result; an explanation is offered below.…”

“…31 In addition, we can also introduce some weights at the gene and SNP levels to incorporate biological knowledge on which genes or SNPs are more likely to be causal. We have focused on testing on a single pathway; an alternative is to take account of possible overlapping or hierarchical structures of some pathways as discussed in Schaid et al 12 These topics warrant future investigation.…”

A Network-Based Kernel Machine Test for the Identification of Risk Pathways in Genome-Wide Association Studies

“…Pan [15] developed a procedure that reduces the multiple-testing burden according to the average distance between genes in a pathway. Others [16,17] have coined methods that aim to identify significantly associated subnetworks. However, all of these methods are based on p-values, which summarize the risk for a disease for whole genes, rather than on raw genotype data.…”

A novel and efficient algorithm for de novo discovery of mutated driver pathways in cancer

“…In light of that Dendrix and other methods have been successfully applied to the TCGA (Kandoth et al ., 2013), it would be interesting to apply our proposed method to on-going large cancer genomics projects. Furthermore, the current problem differs from existing pathway analysis of genome-wide association studies (GWAS) (Wang et al ., 2007; Torkamani et al ., 2007; Schaid et al ., 2012) in two aspects: (i) the current problem is more challenging in the sense that no pathway is given a priori; (ii) however, GWAS data is different with genetic variants (or mutations) present for healthy control subjects, and it is also higher-dimensional with a larger number of genetic variants. It would be interesting to see whether the key concept of mutation exclusivity and associated methodology in the current context can be extended and applied to GWAS for de novo pathway or gene subnetwork (Liu et al ., 2014) discovery to handle genetic heterogeneity.…”