SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder

Hu, Xiaohan; Liu, Qiang; Zhang, Zhao; Li, Zhiqiang; Wang, Shilin; He, Lin; Shi, Yongyong

doi:10.1038/cr.2010.68

Cited by 64 publications

(55 citation statements)

References 9 publications

(7 reference statements)

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“…In the editing phase of this article, it has come to our attention that Hu et al 13 have also developed a strategy involving GPUs to enhance genome-wide significant SNP pair interaction search, quoting a total runtime of 27 h to scan through the Wellcome Trust Case Control Consortium's bipolar disorder data consisting of 500K SNPs. The proposed algorithm by Hu et al helps consolidate the improved time performance using the inherent parallel nature of GPU to search for significance in all possible SNP pairs.…”

Section: Epiblaster Fast Gpu-based Epistasis T Kam-thong Et Almentioning

confidence: 99%

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

et al. 2010

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Detection of epistatic interaction between loci has been postulated to provide a more in-depth understanding of the complex biological and biochemical pathways underlying human diseases. Studying the interaction between two loci is the natural progression following traditional and well-established single locus analysis. However, the added costs and time duration required for the computation involved have thus far deterred researchers from pursuing a genome-wide analysis of epistasis. In this paper, we propose a method allowing such analysis to be conducted very rapidly. The method, dubbed EPIBLASTER, is applicable to case-control studies and consists of a two-step process in which the difference in Pearson's correlation coefficients is computed between controls and cases across all possible SNP pairs as an indication of significant interaction warranting further analysis. For the subset of interactions deemed potentially significant, a second-stage analysis is performed using the likelihood ratio test from the logistic regression to obtain the P-value for the estimated coefficients of the individual effects and the interaction term. The algorithm is implemented using the parallel computational capability of commercially available graphical processing units to greatly reduce the computation time involved. In the current setup and example data sets (211 cases, 222 controls, 299468 SNPs; and 601 cases, 825 controls, 291095 SNPs), this coefficient evaluation stage can be completed in roughly 1 day. Our method allows for exhaustive and rapid detection of significant SNP pair interactions without imposing significant marginal effects of the single loci involved in the pair.

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Section: Epiblaster Fast Gpu-based Epistasis T Kam-thong Et Almentioning

confidence: 99%

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

et al. 2010

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“…Genome-wide studies of pairwise interactions between SNPs have shown promising results (e.g., Hu et al, 2010;Lippert et al, 2013;Wan et al, 2010a;Wan et al, 2010b). For instance, Hemani et al (2014) recently identified and replicated 30 pairwise interactions associated with gene expression levels.…”

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confidence: 99%

Epi2Loc: An R Package to Investigate Two-Locus Epistatic Models

2014

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Epistasis is a growing area of research in genome-wide studies, but the differences between alternative definitions of epistasis remain a source of confusion for many researchers. One problem is that models for epistasis are presented in a number of formats, some of which have difficult-to-interpret parameters. In addition, the relation between the different models is rarely explained. Existing software for testing epistatic interactions between single-nucleotide polymorphisms (SNPs) does not provide the flexibility to compare the available model parameterizations. For that reason we have developed an R package for investigating epistatic and penetrance models, Epi2Loc, to aid users who wish to easily compare, interpret, and utilize models for two-locus epistatic interactions. Epi2Loc facilitates research on SNP-SNP interactions by allowing the R user to easily convert between common parametric forms for two-locus interactions, generate data for simulation studies, and perform power analyses for the selected model with a continuous or dichotomous phenotype. The usefulness of the package for model interpretation and power analysis is illustrated using data on rheumatoid arthritis.

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“…Furthermore, the computational load for preliminary epistasis screenings is not negligible. Accordingly, several tools emerged to speedup this process employing hardware accelerators, such as GPUs in GBOOST [28] or SHEsisEpi [9]. Another way to reduce the computational burden is to reduce the number of SNPs in advance by pre-filtering for linkage disequilibrium (LD), although it can be shown that SNPs supposed to be in LD may also reveal an interaction effect [2,10].…”

Section: Introductionmentioning

confidence: 99%

1,000x Faster than PLINK: Genome-Wide Epistasis Detection with Logistic Regression Using Combined FPGA and GPU Accelerators

Wienbrandt

Kässens

Hübenthal

et al. 2018

EasyChair Preprints

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Abstract. Logistic regression as implemented in PLINK is a powerful and commonly used framework for assessing gene-gene (GxG) interactions. However, fitting regression models for each pair of markers in a genome-wide dataset is a computationally intensive task. Performing billions of tests with PLINK takes days if not weeks, for which reason prefiltering techniques and fast epistasis screenings are applied to reduce the computational burden. Here, we demonstrate that employing a combination of a Xilinx UltraScale KU115 FPGA with an Nvidia Tesla P100 GPU leads to runtimes of only minutes for logistic regression GxG tests on a genome-wide scale. In particular, a dataset of 53,000 samples genotyped at 130,000 SNPs was analyzed in 8 minutes, resulting in a speedup of more than 1,000 when compared to PLINK v1.9 using 32 threads on a server-grade computing platform. Furthermore, on-the-fly calculation of test statistics, p-values and LD-scores in double-precision make commonly used prefiltering strategies obsolete.

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SHEsisEpi, a GPU-enhanced genome-wide SNP-SNP interaction scanning algorithm, efficiently reveals the risk genetic epistasis in bipolar disorder

Cited by 64 publications

References 9 publications

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units

Epi2Loc: An R Package to Investigate Two-Locus Epistatic Models

1,000x Faster than PLINK: Genome-Wide Epistasis Detection with Logistic Regression Using Combined FPGA and GPU Accelerators

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