Cordell, H. J. Detecting gene-gene interactions that underlie human diseases. Nat. Rev. Genet. 10, 392-404

Institute of Human Genetics, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne NE1 3BZ, UK.
Nature Reviews Genetics (Impact Factor: 36.98). 06/2009; 10(6):392-404. DOI: 10.1038/nrg2579
Source: PubMed


Following the identification of several disease-associated polymorphisms by genome-wide association (GWA) analysis, interest is now focusing on the detection of effects that, owing to their interaction with other genetic or environmental factors, might not be identified by using standard single-locus tests. In addition to increasing the power to detect associations, it is hoped that detecting interactions between loci will allow us to elucidate the biological and biochemical pathways that underpin disease. Here I provide a critical survey of the methods and related software packages currently used to detect the interactions between genetic loci that contribute to human genetic disease. I also discuss the difficulties in determining the biological relevance of statistical interactions.

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    • "On the other hand, while IGF2BP2, HHEX and PPARG that were not independently associated, turned out to be significant in the multivariate context. IRS-1 and CAPN10 that were associated individually failed to show association in the multivariate analysis, both these situations conforming probably to the phenomenon of epistasis (Cordell, 2010; Culverhouse et al., 2002); where as in the first case, the genes with no independent effect, turn out to be significant in the presence of other genes, in case of the latter the potential independent effects of IRS-1 and CAPN10 were probably masked by other genes. "
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    Meta Gene 09/2015; 5. DOI:10.1016/j.mgene.2015.05.001
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    • "Previous efforts have already addressed this problem in high-throughput GWAS datasets [4], [5]. Computing epistasis is highly time-consuming due to the large number of pairwise tests to be calculated . "
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    ABSTRACT: Development of new methods to detect pairwise epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important task in bioinformatics as they can help to explain genetic influences on diseases. As these studies are time consuming operations, some tools exploit the characteristics of different hardware accelerators (such as GPUs and Xeon Phi coprocessors) to reduce the runtime. Nevertheless, all these approaches are not able to efficiently exploit the whole computational capacity of modern clusters that contain both GPUs and Xeon Phi coprocessors. In this paper we investigate approaches to map pairwise epistasic detection on heterogeneous clusters using both types of accelerators. The runtimes to analyze the well-known WTCCC dataset consisting of about 500K SNPs and 5K samples on one and two NVIDIA K20m are reduced by 27% thanks to the use of a hybrid approach with one additional single Xeon Phi coprocessor.
    IEEE Transactions on Parallel and Distributed Systems 07/2015; DOI:10.1109/TPDS.2015.2460247 · 2.17 Impact Factor
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    • "The Random Forest algorithm is a tree-based ensemble machine learning tool more suited to detect evidence of polygenic adaptation since it search for correlation and interactions among loci (Goldstein et al. 2011; Boulesteix et al. 2012). The efficiency of Random Forest approach in finding a group of covarying markers that differentiate complex traits has been shown in several medicine and agriculture studies (Shi et al. 2005; Cordell 2009; Tang et al. 2009; Xu et al. 2011; Poland et al. 2012; Mokry et al. 2013; Jarquín et al. 2014) but still infrequent in evolutionary molecular [but see Brieuc et al. 2015; Pavey et al. 2015)]. As recommended by Strobl et al. (2009) and Chen and Ishwaran (2012), a total of 100 forests (runs) sets with different seed numbers were computed to ensure randomness of the test. "
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