PBOOST: a GPU-based tool for parallel permutation tests in genome-wide association studies

Yang, Guangyuan; Jiang, Wei; Yang, Qiang; Yu, Weichuan

doi:10.1093/bioinformatics/btu840

Cited by 14 publications

(10 citation statements)

References 7 publications

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“…Hadoop/Spark is designed to operate on commodity machines, and it should therefore be kept in mind that alternative computing frameworks such as High Performance Computing solutions, or those based on dedicated hardware such as GPUs [72,71,25,18] and FPGAs [68,20], still retain the edge in terms of raw computational capacity. Rather, the main benefits of Hadoop/Spark lie in its robustness to node failure, and its ability to provide an abstraction of the distributed infrastructure.…”

Section: Discussion and Research Perspectivesmentioning

confidence: 99%

“…Few solutions have been designed to address this challenge, that mostly rely on dedicated hardware devices such as Graphical Processing Units (GPUs) [72,71,25,18], or Field-Programmable Gate Array (FPGAs) [68,20]. While these solutions greatly speed up computation times, their use is in practice hindered by the need to acquire specialised and expensive hardware, whose programming is based on low-level and difficult to debug programming languages.…”

Section: Introductionmentioning

confidence: 99%

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DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

Helaers

Lenaerts

et al. 2017

Preprint

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Background:The advent of next-generation sequencing technologies has opened new avenues for clinical genomics research. In particular, as sequencing costs continue to decrease, an ever-growing number of clinical genomics institutes now rely on DNA sequencing studies at varying scales -genome, exome, mendeliome -for uncovering disease-associated variants or genes, in both rare and non-rare diseases.A common methodology for identifying such variants or genes is to rely on genetic association studies (GAS), that test whether allele or genotype frequencies differ between two groups of individuals, usually diseased subjects and healthy controls. Current bioinformatics tools for performing GAS are designed to run on standalone machines, and do not scale well with the increasing size of study designs and the search for multi-locus genetic associations. More efficient distributed and scalable data analysis solutions are needed to address this challenge. Results: We developed a Big Data solution stack for distributing computations in genetic association studies, that address both single and multi-locus associations. The proposed stack, called DiGeST (Distributed Gene/variant Scoring Tool) is divided in two main components: a Hadoop/Spark high-performance computing back-end for efficient data storage and distributed computing, and a Web front-end providing users with a rich set of options to filter, compare and explore exome data from different sample populations. Using exome data

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Section: Discussion and Research Perspectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

Helaers

Lenaerts

et al. 2017

Preprint

View full text Add to dashboard Cite

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“…Some research has been tackled to accelerate WY to enumerate significant SNP pairs associated with a target trait [22,23]. FastChi prunes SNP pairs using the upper bound of the Chi-square test [23].…”

Section: Related Workmentioning

confidence: 99%

“…FastChi prunes SNP pairs using the upper bound of the Chi-square test [23]. PBOOST implemented parallelization of the permutation test on GPU [22]. However, both methods use Chi-square test, whereas Fisher's exact test is recommended to assess the significance of categorical data [13].…”

Section: Related Workmentioning

confidence: 99%

High-speed westfall-young permutation procedure for genome-wide association studies

Terada

Kim

Sese³

2015

Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics

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Genome-wide association studies (GWASs) are widely used to investigate statistically significant associations between diseases and single nucleotide polymorphisms (SNPs) to identify causal factors of diseases. In GWAS, statistical significance of more than one million SNPs have been recently assessed, but in many case, no associations are found because of the application of conservative multiple testing corrections, such as Bonferroni correction. While more sensitive methods, such as Westfall-Young permutation procedure (WY), would relate more SNPs with diseases, its extremely long computational time has prohibited from the application of WY to GWAS. We introduce an algorithm to accelerate WY, named High-speed Westfall-Young permutation procedure (HWY). HWY utilizes three techniques to make WY computationally practical. First, P-value calculations for SNPs that cannot affect the adjusted significance level are pruned. Second, a lookup table of P-values is used to avoid frequent duplicate calculations. Finally, computations are parallelized using a GPGPU. HWY was 619 times faster than WY and more than 122 times faster than PLINK, a widely used GWAS software, and analyzed a dataset contained one million SNPs and one thousand individuals in approximately two hours. Re-analysis of existing GWAS datasets with HWY may uncover additional hidden SNP-trait associations.

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“…To tackle these challenges, some algorithms were developed to detect synergistic SNP combinations associated with complex diseases. The majority of these methods can be classified into three categories: exhaustive methods [ 7 , 8 , 9 , 10 , 11 ], filtering methods (SNPHarvester) [ 12 , 13 ], or artificial intelligence (including swarm intelligence and heuristic search methods) [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

Tuo

2018

Genes

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Detecting high-order epistasis in genome-wide association studies (GWASs) is of importance when characterizing complex human diseases. However, the enormous numbers of possible single-nucleotide polymorphism (SNP) combinations and the diversity among diseases presents a significant computational challenge. Herein, a fast method for detecting high-order epistasis based on an interaction weight (FDHE-IW) method is evaluated in the detection of SNP combinations associated with disease. First, the symmetrical uncertainty (SU) value for each SNP is calculated. Then, the top-k SNPs are isolated as guiders to identify 2-way SNP combinations with significant interaction weight values. Next, a forward search is employed to detect high-order SNP combinations with significant interaction weight values as candidates. Finally, the findings were statistically evaluated using a G-test to isolate true positives. The developed algorithm was used to evaluate 12 simulated datasets and an age-related macular degeneration (AMD) dataset and was shown to perform robustly in the detection of some high-order disease-causing models.

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PBOOST: a GPU-based tool for parallel permutation tests in genome-wide association studies

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 14 publications

References 7 publications

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

DiGeST: Distributed Computing for Scalable Gene and Variant Ranking with Hadoop/Spark

High-speed westfall-young permutation procedure for genome-wide association studies

FDHE-IW: A Fast Approach for Detecting High-Order Epistasis in Genome-Wide Case-Control Studies

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