Dataset Information

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

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.

SUBMITTER: Kam-Thong T

PROVIDER: S-EPMC3117340 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Kam-Thong Tony T Pütz Benno B Karbalai Nazanin N Müller-Myhsok Bertram B Borgwardt Karsten K

Bioinformatics (Oxford, England) 20110701 13

<h4>Motivation</h4>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. ...[more]

PMID: 21685073

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Project description:BackgroundBoolean models are increasingly used to study biological signaling networks. In a Boolean network, nodes represent biological entities such as genes, proteins or protein complexes, and edges indicate activating or inhibiting influences of one node towards another. Depending on the input of activators or inhibitors, Boolean networks categorize nodes as either active or inactive. The formalism is appealing because for many biological relationships, we lack quantitative information about binding constants or kinetic parameters and can only rely on a qualitative description of the type "A activates (or inhibits) B". A central aim of Boolean network analysis is the determination of attractors (steady states and/or cycles). This problem is known to be computationally complex, its most important parameter being the number of network nodes. Various algorithms tackle it with considerable success. In this paper we present an algorithm, which extends the size of analyzable networks thanks to simple and intuitive arguments.ResultsWe present lnet, a software package which, in fully asynchronous updating mode and without any network reduction, detects the fixed states of Boolean networks with up to 150 nodes and a good part of any present cycles for networks with up to half the above number of nodes. The algorithm goes through a complete enumeration of the states of appropriately selected subspaces of the entire network state space. The size of these relevant subspaces is small compared to the full network state space, allowing the analysis of large networks. The subspaces scanned for the analyses of cycles are larger, reducing the size of accessible networks. Importantly, inherent in cycle detection is a classification scheme based on the number of non-frozen nodes of the cycle member states, with cycles characterized by fewer non-frozen nodes being easier to detect. It is further argued that these detectable cycles are also the biologically more important ones. Furthermore, lnet also provides standard Boolean analysis features such as node loop detection.Conclusionslnet is a software package that facilitates the analysis of large Boolean networks. Its intuitive approach helps to better understand the network in question.

Dataset Information

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Publications

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

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