Fastchi: An Efficient Algorithm for Analyzing Gene-Gene Interactions

Abstract

Recent advances in high-throughput genotyping have inspired increasing research interests in genome-wide association study for diseases. To understand underlying biological mechanisms of many diseases, we need to consider simultaneously the genetic effects across multiple loci. The large number of SNPs often makes multilocus association study very computationally challenging because it needs to explicitly enumerate all possible SNP combinations at the genome-wide scale. Moreover, with the large number of SNPs correlated, permutation procedure is often needed for properly controlling family-wise error rates. This makes the problem even more computationally demanding, since the test procedure needs to be repeated for each permuted data. In this paper, we present FastChi, an exhaustive yet efficient algorithm for genome-wide two-locus chi-square test. FastChi utilizes an upper bound of the two-locus chi-square test, which can be expressed as the sum of two terms – both are efficient to compute: the first term is based on the single-locus chi-square test for the given phenotype; and the second term only depends on the genotypes and is independent of the phenotype. This upper bound enables the algorithm to only perform the two-locus chi-square test on a small number of candidate SNP pairs without the risk of missing any significant ones. Since the second part of the upper bound only needs to be precomputed once and stored for subsequence uses, the advantage is more prominent in large permutation tests. Extensive experimental results demonstrate that our method is an order of magnitude faster than the brute force alternative.

A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

“…Grammatical evolution [10] and genetic programming [13] optimizing artificial neural networks architecture and parameters have been studied, while the huge time consuming is a bottleneck for these heuristic methods. Zhang et al proposed FastANOVA [24] and FastChi [25] specially designed for ANOVA test and chi-square test, respectively, to search significant SNP pairs. Zhang et al built a minimum spanning tree on SNPs to speed up the updating process for the contingency tables and proposed a tree-based epistasis detection algorithm to prune a majority of the individuals [26].…”

Efficient algorithms for genome-wide association study

“…In Section 7, we discuss further extensions of the FastANOVA algorithm to case-control study whose phenotypes can be represented as binary variables. We first show that the principle of FastANOVA can be applied to Chi-square test [Zhang et al 2009b]. Then we briefly describe a more general approach that can be applied to a variety of statistics used in case-control study [Zhang et al 2009a].…”

Ultrafast genome-wide scan for SNP–SNP interactions in common complex disease

“…The authors of FastCHI (Zhang et al 2009), FastANOVA (Zhang et al 2008), COE (Zhang et al 2010b) and TEAM presented a review in which TEAM was reported as the most appropriate for handling human data sets, and was therefore chosen to represent the family of methods. TEAM achieves computational speedup by a novel approach that allows it to accurately identify interacting SNP pairs (for most statistical tests) by checking only a small subset of individuals in the cohort.…”

“…Another widely cited method, BEAM (Zhang and Liu 2007), does not scale to present day data sets (Cordell 2009) and was left out of this analysis. There are numerous other methods that perform whole-genome interaction scans (Emily et al 2009;Zhang et al 2009;Greene et al 2010;Liu et al 2011), including some that utilize sampling subsets of individuals for computational speedup (Achlioptas et al 2011). An older review of a few of these is provided elsewhere (Cordell 2009).…”