185 research outputs found
AntEpiSeeker: detecting epistatic interactions for case-control studies using a two-stage ant colony optimization algorithm
<p>Abstract</p> <p>Background</p> <p>Epistatic interactions of multiple single nucleotide polymorphisms (SNPs) are now believed to affect individual susceptibility to common diseases. The detection of such interactions, however, is a challenging task in large scale association studies. Ant colony optimization (ACO) algorithms have been shown to be useful in detecting epistatic interactions.</p> <p>Findings</p> <p>AntEpiSeeker, a new two-stage ant colony optimization algorithm, has been developed for detecting epistasis in a case-control design. Based on some practical epistatic models, AntEpiSeeker has performed very well.</p> <p>Conclusions</p> <p>AntEpiSeeker is a powerful and efficient tool for large-scale association studies and can be downloaded from <url>http://nce.ads.uga.edu/~romdhane/AntEpiSeeker/index.html</url>.</p
Evaluation of Existing Methods for High-Order Epistasis Detection
[Abstract]
Finding epistatic interactions among loci when expressing a phenotype is a widely employed strategy to understand the genetic architecture of complex traits in GWAS. The abundance of methods dedicated to the same purpose, however, makes it increasingly difficult for scientists to decide which method is more suitable for their studies. This work compares the different epistasis detection methods published during the last decade in terms of runtime, detection power and type I error rate, with a special emphasis on high-order interactions. Results show that in terms of detection power, the only methods that perform well across all experiments are the exhaustive methods, although their computational cost may be prohibitive in large-scale studies. Regarding non-exhaustive methods, not one could consistently find epistasis interactions when marginal effects are absent. If marginal effects are present, there are methods that perform well for high-order interactions, such as BADTrees, FDHE-IW, SingleMI or SNPHarvester. As for false-positive control, only SNPHarvester, FDHE-IW and DCHE show good results. The study concludes that there is no single epistasis detection method to recommend in all scenarios. Authors should prioritize exhaustive methods when sufficient computational resources are available considering the data set size, and resort to non-exhaustive methods when the analysis time is prohibitive.10.13039/501100010801-Xunta de Galicia (Grant Number: ED431C2016-037, ED431C2017/04 and ED431G2019/01)
10.13039/501100003176-Ministerio de Educacion Cultura y Deporte (Grant Number: FPU16/01333)
10.13039/501100003329-Ministerio de Economia y Competitividad (Grant Number: CGL2016-75482-P, PID2019-104184RB-I00, AEI/FEDER/EU, 10.13039/50110 and TIN2016-75845-P)Xunta de Galicia; ED431C2016-037Xunta de Galicia; ED431G2019/01Xunta de Galicia; ED431C 2017/0
High-Order Epistasis Detection in High Performance Computing Systems
Programa Oficial de Doutoramento en Investigación en Tecnoloxías da Información. 524V01[Resumo]
Nos últimos anos, os estudos de asociación do xenoma completo (Genome-Wide
Association Studies, GWAS) están a gañar moita popularidade de cara a buscar unha
explicación xenética á presenza ou ausencia de certas enfermidades nos humanos.Hai
un consenso nestes estudos sobre a existencia de interaccións xenéticas que condicionan
a expresión de enfermidades complexas, un fenómeno coñecido como epistasia.
Esta tese céntrase no estudo deste fenómeno empregando a computación de altas
prestacións (High-Performance Computing, HPC) e dende a súa perspectiva estadística:
a desviación da expresión dun fenotipo como a suma dos efectos individuais de
múltiples variantes xenéticas. Con este obxectivo desenvolvemos unha primeira ferramenta,
chamada MPI3SNP, que identifica interaccións de tres variantes a partir dun
conxunto de datos de entrada. MPI3SNP implementa unha busca exhaustiva empregando
un test de asociación baseado na Información Mutua, e explota os recursos de
clústeres de CPUs ou GPUs para acelerar a busca. Coa axuda desta ferramenta avaliamos
o estado da arte da detección de epistasia a través dun estudo que compara o rendemento
de vintesete ferramentas. A conclusión máis importante desta comparativa
é a incapacidade dos métodos non exhaustivos de atopar interacción ante a ausencia
de efectos marxinais (pequenos efectos de asociación das variantes individuais que
participan na epistasia). Por isto, esta tese continuou centrándose na optimización da
busca exhaustiva de epistasia. Por unha parte, mellorouse a eficiencia do test de asociación
a través dunha implantación vectorial do mesmo. Por outro lado, creouse un
algoritmo distribuído que implementa unha busca exhaustiva capaz de atopar epistasia
de calquera orden. Estes dous fitos lógranse en Fiuncho, unha ferramenta que integra
toda a investigación realizada, obtendo un rendemento en clústeres de CPUs que
supera a todas as súas alternativas no estado da arte. Adicionalmente, desenvolveuse
unha libraría para simular escenarios biolóxicos con epistasia chamada Toxo. Esta
libraría permite a simulación de epistasia seguindo modelos de interacción xenética
existentes para orde alto.[Resumen]
En los últimos años, los estudios de asociación del genoma completo (Genome-
Wide Association Studies, GWAS) están ganando mucha popularidad de cara a buscar
una explicación genética a la presencia o ausencia de ciertas enfermedades en los seres
humanos. Existe un consenso entre estos estudios acerca de que muchas enfermedades
complejas presentan interacciones entre los diferentes genes que intervienen en su
expresión, un fenómeno conocido como epistasia. Esta tesis se centra en el estudio de
este fenómeno empleando la computación de altas prestaciones (High-Performance
Computing, HPC) y desde su perspectiva estadística: la desviación de la expresión de
un fenotipo como suma de los efectos de múltiples variantes genéticas. Para ello se
ha desarrollado una primera herramienta, MPI3SNP, que identifica interacciones de
tres variantes a partir de un conjunto de datos de entrada. MPI3SNP implementa una
búsqueda exhaustiva empleando un test de asociación basado en la Información Mutua,
y explota los recursos de clústeres de CPUs o GPUs para acelerar la búsqueda.
Con la ayuda de esta herramienta, hemos evaluado el estado del arte de la detección
de epistasia a través de un estudio que compara el rendimiento de veintisiete herramientas.
La conclusión más importante de esta comparativa es la incapacidad de los
métodos no exhaustivos de localizar interacciones ante la ausencia de efectos marginales
(pequeños efectos de asociación de variantes individuales pertenecientes a una
relación epistática). Por ello, esta tesis continuó centrándose en la optimización de la
búsqueda exhaustiva. Por un lado, se mejoró la eficiencia del test de asociación a través
de una implementación vectorial del mismo. Por otra parte, se diseñó un algoritmo
distribuido que implementa una búsqueda exhaustiva capaz de encontrar relaciones
epistáticas de cualquier tamaño. Estos dos hitos se logran en Fiuncho, una herramienta
que integra toda la investigación realizada, obteniendo un rendimiento en clústeres
de CPUs que supera a todas sus alternativas del estado del arte. A mayores, también se
ha desarrollado una librería para simular escenarios biológicos con epistasia llamada
Toxo. Esta librería permite la simulación de epistasia siguiendomodelos de interacción
existentes para orden alto.[Abstract]
In recent years, Genome-Wide Association Studies (GWAS) have become more and
more popular with the intent of finding a genetic explanation for the presence or absence
of particular diseases in human studies. There is consensus about the presence
of genetic interactions during the expression of complex diseases, a phenomenon
called epistasis. This thesis focuses on the study of this phenomenon, employingHigh-
Performance Computing (HPC) for this purpose and from a statistical definition of the
problem: the deviation of the expression of a phenotype from the addition of the individual
contributions of genetic variants. For this purpose, we first developedMPI3SNP,
a programthat identifies interactions of three variants froman input dataset. MPI3SNP
implements an exhaustive search of epistasis using an association test based on the
Mutual Information and exploits the resources of clusters of CPUs or GPUs to speed up
the search. Then, we evaluated the state-of-the-art methods with the help of MPI3SNP
in a study that compares the performance of twenty-seven tools. The most important
conclusion of this study is the inability of non-exhaustive approaches to locate epistasis
in the absence of marginal effects (small association effects of individual variants
that partake in an epistasis interaction). For this reason, this thesis continued focusing
on the optimization of the exhaustive search. First, we improved the efficiency of
the association test through a vector implementation of this procedure. Then, we developed
a distributed algorithm capable of locating epistasis interactions of any order.
These two milestones were achieved in Fiuncho, a program that incorporates all the
research carried out, obtaining the best performance in CPU clusters out of all the alternatives
of the state-of-the-art. In addition, we also developed a library to simulate
particular scenarios with epistasis called Toxo. This library allows for the simulation of
epistasis that follows existing interaction models for high-order interactions
Discovering Higher-order SNP Interactions in High-dimensional Genomic Data
In this thesis, a multifactor dimensionality reduction based method on associative classification is employed to identify higher-order SNP interactions for enhancing the understanding of the genetic architecture of complex diseases. Further, this thesis explored the application of deep learning techniques by providing new clues into the interaction analysis. The performance of the deep learning method is maximized by unifying deep neural networks with a random forest for achieving reliable interactions in the presence of noise
Grammatical evolution decision trees for detecting gene-gene interactions
<p>Abstract</p> <p>Background</p> <p>A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.</p> <p>Methods</p> <p>Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions.</p> <p>Results</p> <p>The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects.</p> <p>Conclusions</p> <p>GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.</p
Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions
<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF).</p> <p>Results</p> <p>SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm.</p> <p>Conclusion</p> <p>Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from <url>http://www.epistasis.org</url>.</p
Ant Colony Optimisation for Exploring Logical Gene-Gene Associations in Genome Wide Association Studies.
In this paper a search for the logical variants of gene-gene
interactions in genome-wide association study (GWAS) data using ant
colony optimisation is proposed. The method based on stochastic algorithms
is tested on a large established database from the Wellcome
Trust Case Control Consortium and is shown to discover logical operations
between combinations of single nucleotide polymorphisms that can
discriminate Type II diabetes. A variety of logical combinations are explored
and the best discovered associations are found within reasonable
computational time and are shown to be statistically significantThis study makes use of data generated by the Wellcome Trust Case Control
Consortium. A full list of the investigators who contributed to the generation
of the data is available from http://www.wtccc.org.uk. Funding for the project
was provided by the Wellcome Trust under award 076113.
The work contained in this paper was funded by an EPSRC First Grant
(EP/J007439/1) and we acknowledge their kind support
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