NDLI: Prioritization of genomic locus pairs for testing epistasis

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Prioritization of genomic locus pairs for testing epistasis

Content Provider	ACM Digital Library
Author	Ayati, Marzieh Koyutürk, Mehmet
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.
Starting Page	240
Ending Page	248
Page Count	9
File Format	PDF
ISBN	9781450328944
DOI	10.1145/2649387.2649449
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical significance Genome-wide association studies Epistasis
Content Type	Text
Resource Type	Article

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