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Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Kam-Thong T, Pütz B, Karbalai N, Müller-Myhsok B, Borgwardt K - Bioinformatics (2011)

Bottom Line: The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs.The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.

View Article: PubMed Central - PubMed

Affiliation: Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de

ABSTRACT

Motivation: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.

Results: In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert-Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.

Availability: The program is available at http://www.mpipsykl.mpg.de/epigpuhsic/.

Contact: tony@mpipsykl.mpg.de.

Show MeSH

Related in: MedlinePlus

Overall fit done on the top one million matching pairs. −log10 Linear regression interaction model versus HSIC.
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Figure 3: Overall fit done on the top one million matching pairs. −log10 Linear regression interaction model versus HSIC.

Mentions: In Figure 3, the HSIC values are plotted against the P-values of the linear regression on the interaction term. Furthermore, it has been shown that the distribution of HSIC is asymptotically approaching normal (Gretton et al., 2005), which in turn allows for significance tests to be performed based on standard statistics. The P-values of the HSIC terms are compared to the P-values of the linear regression on the interaction term (Fig. 4).Fig. 3.


Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Kam-Thong T, Pütz B, Karbalai N, Müller-Myhsok B, Borgwardt K - Bioinformatics (2011)

Overall fit done on the top one million matching pairs. −log10 Linear regression interaction model versus HSIC.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117340&req=5

Figure 3: Overall fit done on the top one million matching pairs. −log10 Linear regression interaction model versus HSIC.
Mentions: In Figure 3, the HSIC values are plotted against the P-values of the linear regression on the interaction term. Furthermore, it has been shown that the distribution of HSIC is asymptotically approaching normal (Gretton et al., 2005), which in turn allows for significance tests to be performed based on standard statistics. The P-values of the HSIC terms are compared to the P-values of the linear regression on the interaction term (Fig. 4).Fig. 3.

Bottom Line: The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs.The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.

View Article: PubMed Central - PubMed

Affiliation: Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany. tony@mpipsykl.mpg.de

ABSTRACT

Motivation: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.

Results: In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert-Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.

Availability: The program is available at http://www.mpipsykl.mpg.de/epigpuhsic/.

Contact: tony@mpipsykl.mpg.de.

Show MeSH
Related in: MedlinePlus