GBOOST: a GPU-based tool for detecting gene–gene interactions in genome–wide case control studies

Yung, Ling Sing; Yang, Can; Wan, Xiang; Yu, Weichuan

doi:10.1093/bioinformatics/btr114

Cited by 146 publications

(115 citation statements)

References 4 publications

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“…The tree-building algorithm can be accelerated further by noticing that the evaluation of splitting rules at each given tree node is parallelizable. We are currently working on a GPU (graphic processing unit) implementation to take advantage of the parallelizability (Greene et al, 2010;Yung et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

A fast and powerful tree-based association test for detecting complex joint effects in case–control studies

et al. 2014

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Motivation: Multivariate tests derived from the logistic regression model are widely used to assess the joint effect of multiple predictors on a disease outcome in case-control studies. These tests become less optimal if the joint effect cannot be approximated adequately by the additive model. The tree-structure model is an attractive alternative, as it is more apt to capture non-additive effects. However, the tree model is used most commonly for prediction and seldom for hypothesis testing, mainly because of the computational burden associated with the resampling-based procedure required for estimating the significance level. Results: We designed a fast algorithm for building the tree-structure model and proposed a robust TREe-based Association Test (TREAT) that incorporates an adaptive model selection procedure to identify the optimal tree model representing the joint effect. We applied TREAT as a multilocus association test on420 000 genes/regions in a study of esophageal squamous cell carcinoma (ESCC) and detected a highly significant novel association between the gene CDKN2B and ESCC (P ¼ 6:0 Â 10 À8 ). We also demonstrated, through simulation studies, the power advantage of TREAT over other commonly used tests.

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Section: Discussionmentioning

confidence: 99%

A fast and powerful tree-based association test for detecting complex joint effects in case–control studies

et al. 2014

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“…The extension of this method to GPUs as described in Yung et al [14] is approximately 75 times faster than GLIDE. However, GLIDE can be substituted or complemented by GBOOST if, and only if, both the phenotype and the genotype data are in the discrete domain.…”

Section: Discussionmentioning

confidence: 99%

“…Several software tools designed to perform epistasis searches on GPUs, such as SHEsisEpi [11] , EPIBLASTER [12] , EPIGPUHSIC [13] and GBOOST [14] , have recently been proposed and demonstrated substantial advantages of the use of GPU in this application. However, they are either restricted to binary or discrete phenotypes, which limits the scope of data sets they can analyze, or neglect main effects, which hinders the overall interpretation of their results.…”

Section: Glide: Gpu-based Linear Regression For Detection Of Epistasismentioning

confidence: 99%

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Kam‐Thong¹,

Azencott²,

Cayton

et al. 2012

Hum Hered

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Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year’s time to complete the same task.

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“…[11,12,13]). Furthermore, it is faster than previous approaches not only for CPU but also for GPU computation (GBOOST [14]). …”

Section: Introductionmentioning

confidence: 92%

Hybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS

González-Domínguez

Schmidt

Kässens

et al. 2014

Lecture Notes in Computer Science

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Abstract. High-throughput genotyping technologies allow the collection of up to a few million genetic markers (such as SNPs) of an individual within a few minutes of time. Detecting epistasis, such as 2-SNP interactions, in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. In this work we present EpistSearch, a parallelized tool that, following the log-linear model approach, uses a novel filter to determine the interactions between all SNP-pairs. Our tool is parallelized using a hybrid combination of Pthreads and CUDA in order to take advantage of CPU/GPU architectures. Experimental results with simulated and real datasets show that EpistSearch outperforms previous approaches, either using GPUs or only CPU cores. For instance, an exhaustive analysis of a real-world dataset with 500,000 SNPs and 5,000 individuals requires less than 42 minutes on a machine with 6 CPU cores and a GTX Titan GPU.

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GBOOST: a GPU-based tool for detecting gene–gene interactions in genome–wide case control studies

Abstract: GBOOST code is available at http://bioinformatics.ust.hk/BOOST.html#GBOOST.

Cited by 146 publications

References 4 publications

A fast and powerful tree-based association test for detecting complex joint effects in case–control studies

A fast and powerful tree-based association test for detecting complex joint effects in case–control studies

GLIDE: GPU-Based Linear Regression for Detection of Epistasis

Hybrid CPU/GPU Acceleration of Detection of 2-SNP Epistatic Interactions in GWAS

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