Cloud Computing for Detecting High-Order Genome-Wide Epistatic Interaction via Dynamic Clustering

Backgroud: Taking the advan tage of high-throughput single nucleotide polymorphism (SNP) genotyping technology, large genome-wide association studies (GWASs) have been considered to hold promise for unravelling complex relationships between genotype and phenotype. At present, traditional single-locus-based methods are insufficient to detect interactions consisting of multiple-locus, which are broadly existing in complex traits. In addition, statistic tests for high order epistatic interactions with more than 2 SNPs propose computational and analytical challenges because the computation increases exponentially as the cardinality of SNPs combinations gets larger. Results: In this paper, we provide a simple, fast and powerful method using dynamic clustering and cloud computing to detect genome-wide multi-locus epistatic interactions. We have constructed systematic experiments to compare powers performance against some recently proposed algorithms, including TEAM, SNPRuler, EDCF and BOOST. Furthermore, we have applied our method on two real GWAS datasets, Age-related macular degeneration (AMD) and Rheumatoid arthritis (RA) datasets, where we find some novel potential disease-related genetic factors which are not shown up in detections of 2-loci epistatic interactions. Conclusions: Experimental results on simulated data demonstrate that our method is more powerful than some recently proposed methods on both two- and three-locus disease models. Our method has discovered many novel high-order associations that are significantly enriched in cases from two real GWAS datasets. Moreover, the running time of the cloud implementation for our method on AMD dataset and RA dataset are roughly 2 hours and 50 hours on a cluster with forty small virtual machines for detecting two-locus interactions, respectively. Therefore, we believe that our method is suitable and effective for the full-scale analysis of multiple-locus epistatic interactions in GWAS

Searching Genome-wide Disease Association Through SNP Data

Taking the advantage of the high-throughput Single Nucleotide Polymorphism (SNP) genotyping technology, Genome-Wide Association Studies (GWASs) are regarded holding promise for unravelling complex relationships between genotype and phenotype. GWASs aim to identify genetic variants associated with disease by assaying and analyzing hundreds of thousands of SNPs. Traditional single-locus-based and two-locus-based methods have been standardized and led to many interesting findings. Recently, a substantial number of GWASs indicate that, for most disorders, joint genetic effects (epistatic interaction) across the whole genome are broadly existing in complex traits. At present, identifying high-order epistatic interactions from GWASs is computationally and methodologically challenging. My dissertation research focuses on the problem of searching genome-wide association with considering three frequently encountered scenarios, i.e. one case one control, multi-cases multi-controls, and Linkage Disequilibrium (LD) block structure. For the first scenario, we present a simple and fast method, named DCHE, using dynamic clustering. Also, we design two methods, a Bayesian inference based method and a heuristic method, to detect genome-wide multi-locus epistatic interactions on multiple diseases. For the last scenario, we propose a block-based Bayesian approach to model the LD and conditional disease association simultaneously. Experimental results on both synthetic and real GWAS datasets show that the proposed methods improve the detection accuracy of disease-specific associations and lessen the computational cost compared with current popular methods

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

An Integrated Approach to Exploit Linkage Disequilibrium for Ultra High Dimensional Genome-wide Data

This paper presents improved methods for analysis of genome-wide association studies in contemporary genetic research. Thanks to current sequencing methods, half to one million single-nucleotide polymorphisms (SNPs) can be feasibly generated within any given population, and there are often correlations among SNPs that cause truly causative loci to be confounded by correlated neighboring loci. Additionally, complex traits are often jointly affected by multiple genetic variants with each having small or moderate individual effects. To address these issues in genome-wide association studies, we propose a novel statistical approach, DCRR, to detect significant associations between large numbers of SNPs and phenotypes. We applied DCRR on simulations of that varied in marker allele frequencies, linkage disequilibrium, and the numbers of SNPs considered; and we analyzed a previously published Arabidopsis thaliana dataset of an AvrRpm1 binary trait. Our distance correlation was effective in ranking SNPs while the logistic ridge regression detected causative SNPs without including spurious correlated neighbors. Our results indicate that DCRR is an effective and reliable method that can improve the accuracy and efficiency of large association datasets

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

Computational Methods for Compositional Epistasis Detection

In genetics, the term “epistasis” refers to the phenomenon that the effect of one gene or single-nucleotide polymorphism (SNP) is dependent on the presence of others. Various possibilities of epistasis exist, and the understanding of them is limited. In recent years, failure of replication for single-locus effects in genome-wide association studies (GWAS) motivates the exploration of epistasis for human complex disease. This thesis is thus dedicated to the study of computational approaches for two-way compositional epistasis (SNP-SNP interaction) detection. Epistasis of this sort is best described by disease models, which can be simply understood as disease probability patterns associated with the genotype combinations of SNP-pairs. Because the epistasis detection problem requires determination of proper disease models to capture the compositional epistasis effect, it is more complicated than a typical variable selection task. Three projects are pursued in this thesis. The first two target epistasis that is characterized by a set of “two-locus, two-allele, two-phenotype and complete-penetrance” (TTTC) disease model, and the third one extends to more general epistasis. There are theoretically 2^9 = 512 TTTC disease models. For a given SNP-pair, the first step of the problem is to find a proper TTTC model to capture its epistasis effect. It is found that existing methods that use data to determine best-fitting disease models prior to screening may be too greedy. Motivated by this, the first project proposes a less greedy strategy by limiting the search of disease models to a set of prototypes. The prototypes are determined a priori. Specifically, a distance metric is defined and used to cluster all disease models, and then a “representative” from each cluster is selected to form the prototypes. Compared to the existing approaches, the proposed method provides a more satisfying balance between precision and recall in epistasis detection. If one uses data to determine a best-fitting disease model for a pair of SNPs, the nominal statistical evidence of association between the SNP-pair and the disease outcome is inflated. Therefore, the second project aims to directly correct inflation of this type. To make it feasible for genome-wide studies, a first-order correction method is proposed that can be applied in practice with no additional computational cost. Simulation studies are performed on two popular existing methods, which show that the correction is quite effective in improving an overall epistasis detection. The TTTC disease models can be viewed as coding two risk levels, i.e., high and low risk. Compared to them, some other disease models code multiple risk levels, which capture more general epistasis patterns. Two methods are proposed in the third project, which are centered on epistasis detection using multi-level risk disease models. One method is inspired by the fused lasso under a regression-based framework, and adopts the post-model selection test to account for inflation incurred during disease model searching. The other one makes sequential split of the genotype combinations of a SNP-pair and uses a stopping criterion to determine the final disease model; after that, it also applies a first-order correction to the testing statistic to effectively account for inflation. It is shown that the two methods with totally different starting framework are equivalent in terms of the disease model searching process. Subsequent simulation studies show that use of multi-level disease models achieves better detection efficiency in terms of a balance between precision and recall than the two-level ones. In summary, it is a rather complicated task to uncover the underlying mechanism of locus interaction effects, and endeavours are only beginning to be made. The epistasis detection methods in this thesis are practically useful at genome-wide level, which complements the single SNP screening in genome-wide association studies. What’s more, the method of first-order correction for inflation is simple and effective, which is practically valuable for the epistasis detection methods involving inflated testing statistics

FPGAs in Bioinformatics: Implementation and Evaluation of Common Bioinformatics Algorithms in Reconfigurable Logic

Life. Much effort is taken to grant humanity a little insight in this fascinating and complex but fundamental topic. In order to understand the relations and to derive consequences humans have begun to sequence their genomes, i.e. to determine their DNA sequences to infer information, e.g. related to genetic diseases. The process of DNA sequencing as well as subsequent analysis presents a computational challenge for recent computing systems due to the large amounts of data alone. Runtimes of more than one day for analysis of simple datasets are common, even if the process is already run on a CPU cluster. This thesis shows how this general problem in the area of bioinformatics can be tackled with reconfigurable hardware, especially FPGAs. Three compute intensive problems are highlighted: sequence alignment, SNP interaction analysis and genotype imputation. In the area of sequence alignment the software BLASTp for protein database searches is exemplarily presented, implemented and evaluated.SNP interaction analysis is presented with three applications performing an exhaustive search for interactions including the corresponding statistical tests: BOOST, iLOCi and the mutual information measurement. All applications are implemented in FPGA-hardware and evaluated, resulting in an impressive speedup of more than in three orders of magnitude when compared to standard computers. The last topic of genotype imputation presents a two-step process composed of the phasing step and the actual imputation step. The focus lies on the phasing step which is targeted by the SHAPEIT2 application. SHAPEIT2 is discussed with its underlying mathematical methods in detail, and finally implemented and evaluated. A remarkable speedup of 46 is reached here as well

Estudio pan-genómico pronóstico en el cáncer de vejiga = geneome-wide prognostic study in bladder cancer

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina Preventiva, Salud Pública y Microbiología. Fecha de lectura: 7 de Junio de 2013