Using Biological Knowledge to Uncover the Mystery in the Search for Epistasis in Genome-Wide Association Studies

Abstract

The search for the missing heritability in genome-wide association studies (GWAS) has become an important focus for the human genetics community. One suspected location of these genetic effects is in gene-gene interactions, or epistasis. The computational burden of exploring gene-gene interactions in the wealth of data generated in GWAS, along with small to moderate sample sizes, have led to epistasis being an afterthought, rather than a primary focus of GWAS analyses. In this review, we discuss some potential approaches to filter a GWAS dataset to a smaller, more manageable dataset where searching for epistasis is considerably more feasible. We describe a number of alternative approaches, but primarily focus on the use of prior biological knowledge from databases in the public domain to guide the search for epistasis. The manner in which prior knowledge is incorporated into a GWA study can be many and these data can be extracted from a variety of database sources. We discuss a number of these approaches and propose that a comprehensive approach will likely be most fruitful for searching for epistasis in large-scale genomic studies of the current state-of-the-art and into the future.

Epistasis amongst PTPN2 and genes of the vitamin D pathway contributes to risk of juvenile idiopathic arthritis

“…Our data support the utility of applying pathway-based biological hypothesis-driven searches for genetic interactions across genome-wide SNP data [38,39]. Whilst there is relative consensus that epistasis will likely be important in accounting for the 'missing heritability' observed across complex diseases [40], genome-wide epistasis efforts have been hampered by computational complexity, and multiple testing burden [25].…”

Systems Biology Data Analysis Methodology in Pharmacogenomics

“…This dovetails perfectly with our increasing appreciation of the importance of higher levels of genetic control (epigenetic, transcriptomic and so on). A comprehensive review of epistasis-capturing methods (with primary emphasis on GWAS, and using prior knowledge to alleviate associated computational burden) can be found in [12]. In general, their algorithmic foundation is a variable (SNP) selection ‘wrapper’ [13], wherein numerous combinations (models) of SNPs are scored based on the strength of association with a phenotype using an explicitly defined genetic model, and the highest-scoring subsets of interacting SNPs are selected via exhaustive (for lower-order interactions) or heuristic (for higher-order iterations) search through the model space.…”

Prioritization of genomic locus pairs for testing epistasis

“…To alleviate this problem, the number of hypotheses being tested are usually reduced by focusing on pairs of loci that are functionally associated through regulatory elements, pathways, protein interactions, and other functional annotations [11,18]. Some algorithms also prune out certain pairs of loci based on their allelic distributions in the case and control populations, but without explicitly testing them for epistasis (e.g., TEAM [27], QMDR [9], SNPHarvester [24]).…”