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CARAT-GxG: CUDA-Accelerated Regression Analysis Toolkit for Large-Scale Gene-Gene Interaction with GPU Computing System.

Lee S, Kwon MS, Park T - Cancer Inform (2014)

Bottom Line: Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques.In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager.We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.

ABSTRACT
In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene-gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

No MeSH data available.


Related in: MedlinePlus

The red, blue, and green lines indicate the number of combinations that change in rank, vanish, and do not change in rank as the number of iterations increases, respectively.
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Related In: Results  -  Collection


getmorefigures.php?uid=PMC4263399&req=5

f3-cin-suppl.7-2014-027: The red, blue, and green lines indicate the number of combinations that change in rank, vanish, and do not change in rank as the number of iterations increases, respectively.

Mentions: The other comparison was made from the difference of rank, sorted by P-value for all possible iterations. Since our program supports additional optimization methods, it is possible that the restriction of the possible number of iterations seriously affects the performance. To confirm this, we checked how much the rank has changed from the full iteration, as shown in Figure 3. The number of conserved ranks was almost identical after 11 iterations, and a list of significant interactions became identical over 10 iterations.


CARAT-GxG: CUDA-Accelerated Regression Analysis Toolkit for Large-Scale Gene-Gene Interaction with GPU Computing System.

Lee S, Kwon MS, Park T - Cancer Inform (2014)

The red, blue, and green lines indicate the number of combinations that change in rank, vanish, and do not change in rank as the number of iterations increases, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4263399&req=5

f3-cin-suppl.7-2014-027: The red, blue, and green lines indicate the number of combinations that change in rank, vanish, and do not change in rank as the number of iterations increases, respectively.
Mentions: The other comparison was made from the difference of rank, sorted by P-value for all possible iterations. Since our program supports additional optimization methods, it is possible that the restriction of the possible number of iterations seriously affects the performance. To confirm this, we checked how much the rank has changed from the full iteration, as shown in Figure 3. The number of conserved ranks was almost identical after 11 iterations, and a list of significant interactions became identical over 10 iterations.

Bottom Line: Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques.In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager.We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

View Article: PubMed Central - PubMed

Affiliation: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.

ABSTRACT
In genome-wide association studies (GWAS), regression analysis has been most commonly used to establish an association between a phenotype and genetic variants, such as single nucleotide polymorphism (SNP). However, most applications of regression analysis have been restricted to the investigation of single marker because of the large computational burden. Thus, there have been limited applications of regression analysis to multiple SNPs, including gene-gene interaction (GGI) in large-scale GWAS data. In order to overcome this limitation, we propose CARAT-GxG, a GPU computing system-oriented toolkit, for performing regression analysis with GGI using CUDA (compute unified device architecture). Compared to other methods, CARAT-GxG achieved almost 700-fold execution speed and delivered highly reliable results through our GPU-specific optimization techniques. In addition, it was possible to achieve almost-linear speed acceleration with the application of a GPU computing system, which is implemented by the TORQUE Resource Manager. We expect that CARAT-GxG will enable large-scale regression analysis with GGI for GWAS data.

No MeSH data available.


Related in: MedlinePlus