Prioritization of genomic locus pairs for testing epistasis

Abstract

In recent years, genome-wide association studies (GWAS) have successfully identified loci that harbor genetic variants associated with complex diseases. However, susceptibility loci identified by GWAS so far generally account for a limited fraction of heritability in patient populations. More recently, there has been considerable attention on identifying epistatic interactions. However, the large number of pairs to be tested for epistasis poses significant challenges, in terms of both computational (run-time) and statistical (multiple hypothesis testing) considerations.In this paper, we propose a new method to reduce the number of tests required to identify epistatic pairs of genomic loci. The key idea of the proposed algorithm is to reduce the data by identifying sets of loci that may be complementary in their association with the disease. Namely, we identify population covering locus sets (PoCos), i.e., sets of loci that harbor at least one susceptibility allele in samples with the phenotype of interest. Then we compute representative genotypes for PoCos, and assess the significance of the interactions between pairs of PoCos. We use the results of this assessment to prioritize pairs of loci to be tested for epistasis. We test the proposed method on two independent GWAS data sets of Type 2 Diabetes (T2D). Our experimental results show that the proposed method reduces the number of hypotheses to be tested drastically, enabling efficient identification of more epistatic loci that are statistically significant. Moreover, some of the identified epistatic pairs of loci are reproducible between the two datasets. We also show that the proposed method outperforms an existing method for prioritization of locus pairs.

Potpourri: An Epistasis Test Prioritization Algorithm via Diverse SNP Selection

“…Population Co-covering For Ranking SNP Pairs We propose a new strategy which aims at maximizing the population coverage of co-occuring SNPs. Population cover for epistasis test prioritization was first proposed by (18). This approach selects multiple exclusive groups of SNPs (P OCOs) that covers the case cohort.…”

“…The first algorithm of this kind is iLOCi (12), which ranks SNP pairs by performing a dependence test and avoiding pairs that are unrelated to disease but might seem related due to LD. This work was followed by (18) who proposed testing pairs of SNPs in population covering locus sets -POCOs. First, the algorithm greedily selects multiple exclusive groups of SNPs that cover all affected individuals.…”

“…That is, each case sample has to have at least one SNP in each POCO. Epistasis tests then are performed across POCOs with the hope that independent coverage of the cases will lead different POCOs to include complementary and epistatic SNPs (18,19). Finally, Cowman and Koyuturk, introduced the LINDEN algorithm (20).…”

Antimicrobial Resistance Strategies: Are We Approaching the End?

“…Epistasis can considerably affect antimicrobial resistance. Moreover, the combinations of resistance mutations may crucially affect the development of multidrug resistance 63 .…”

Prioritizing tests of epistasis through hierarchical representation of genomic redundancies

“…These methods aim to strike a balance between the accuracy of exhaustive methods and reduced computational complexity of filtering ( 15 , 16 ) and usually either prescreen loci or prioritize pairs of loci for testing epistasis. ( 17 , 18 ). The tool-set PLINK ( 19 , 20 ) contains what is to our knowledge, the fastest general purpose pairwise epistasis scan currently available, fast-epistasis .…”