Abstract
Taking advantage of the high-throughput genotyping technology of Single Nucleotide Polymorphism (SNP), Genome-Wide Association Studies (GWASs) have been successfully implemented for defining the relative role of genes and the environment in disease risk, assisting in enabling preventative and precision medicine. However, current multi-locus-based methods are insufficient in terms of computational cost and discrimination power to detect statistically significant interactions with different genetic effects on multifarious diseases. Statistical tests for multi-locus interactions (≥2 SNPs) raise huge analytical challenges because computational cost increases exponentially as the growth of the cardinality of SNPs in an interaction module. In this paper, we develop a simple, fast, and powerful method, named JS-MA, based on Jensen-Shannon divergence and agglomerative hierarchical clustering, to detect the genome-wide multi-locus interactions associated with multiple diseases. From the systematical simulation, JS-MA is more powerful and efficient compared with the state-of-the-art association mapping tools. JS-MA was applied to the real GWAS datasets for two common diseases, i.e., Rheumatoid Arthritis and Type 1 Diabetes. The results showed that JS-MA not only confirmed recently reported, biologically meaningful associations, but also identified novel multi-locus interactions. Therefore, we believe that JS-MA is suitable and efficient for a full-scale analysis of multi-disease-related interactions in the large GWASs.
Highlights
Genome-wide association studies (GWASs) have been proved to be a powerful tool to identify the genetic susceptibility of associations between a trait of interests using statistical tests (Sabaa et al, 2013)
We present a heuristic method, named JS-AM, based on Jensen-Shannon divergence and agglomerative hierarchical clustering to select a set of candidate Single Nucleotide Polymorphism (SNP) that potentially have effects on multiple phenotypic traits (Guo, 2015)
We present the results of JS-MA on two real GWAS datasets from WTCCC (Zeggini et al, 2007), i.e., Rheumatoid Arthritis (RA) and Type 1 Diabetes (T1D)
Summary
Genome-wide association studies (GWASs) have been proved to be a powerful tool to identify the genetic susceptibility of associations between a trait of interests using statistical tests (Sabaa et al, 2013). Recent studies have confirmed that single nucleotide polymorphisms (SNPs) are associated with a variety of common diseases (Peter and Hunter, 2009). The current primary research paradigm in GWASs is dominated by analyzing the susceptibility of single SNP to one disease at a time. Many studies have demonstrated that epistasis is an important contributor to genetic variation in complex diseases. Most common diseases, such as obesity (Cordell, 2009), cancer (Ritchie et al, 2001), diabetes (Wang et al, 2012), and heart disease (Nelson et al, 2001), are complex traits, which result from a joint effect of various genetic
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