Gene-environment and gene-gene interactions in myopia

Motivated by the release of the UK Biobank data and the lack of documented gene-environment (GxE) and gene-gene (GxG) interactions in myopia, I sought to apply various statistical tools to provide a quantitative assessment of the interplay between environmental and genetic risk factors shaping refractive error. The comparison between the two different risk measurement scales with which GxE interactions can be identified suggested that the additive risk scale can lead to a more informative perspective about refractive error aetiology. The evaluation of two indirect methods for detecting genetic variants affecting refractive error via interaction effects suggested the enrichment of GxG and GxE among the variants that display marginal SNP effects. For genetic variants already known from prior GWAS studies to influence refractive error, genetic effect sizes were highly non-uniform; individuals from the tails of the refractive error distribution (i.e. high myopes and hyperopes) displayed much larger effects compared to individuals in the middle of the distribution (i.e. emmetropes). Prediction of refractive error using GxE interactions indicated that although some of the variance of refractive error could be explained by a risk score constructed using interaction effects, the contribution of GxE was already accounted for by a risk score constructed using marginal SNP effects only. Although a handful of candidate genes were identified using multifactor dimensionality reduction technique, none displayed compelling evidence of involvement in a GxG interaction. There was, however, suggestive evidence that the candidate genes constitute a genetic interaction network which is regulated by hub gene ZMAT4. In summary, the analyses reported in this thesis provide further support for the challenging nature of definitively identifying loci involved in GxE and GxG interactions. The thesis provides several guidelines that future studies could take into account to obtain more insightful results regarding the extent of interactions in refractive error

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

Algorithms for regression and classification

Regression and classification are statistical techniques that may be used to extract rules and patterns out of data sets. Analyzing the involved algorithms comprises interdisciplinary research that offers interesting problems for statisticians and computer scientists alike. The focus of this thesis is on robust regression and classification in genetic association studies. In the context of robust regression, new exact algorithms and results for robust online scale estimation with the estimators Qn and Sn and for robust linear regression in the plane with the estimator least quartile difference (LQD) are presented. Additionally, an evolutionary computation algorithm for robust regression with different estimators in higher dimensions is devised. These estimators include the widely used least median of squares (LMS) and least trimmed squares (LTS). For classification in genetic association studies, this thesis describes a Genetic Programming algorithm that outpeforms the standard approaches on the considered data sets. It is able to identify interesting genetic factors not found before in a data set on sporadic breast cancer and to handle larger data sets than the compared methods. In addition, it is extendible to further application fields

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Large space structures and systems in the space station era: A bibliography with indexes (supplement 04)

Bibliographies and abstracts are listed for 1211 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 Jul. and 30 Dec. 1991. Its purpose is to provide helpful information to the researcher, manager, and designer in technology development and mission design according to system, interactive analysis and design, structural concepts and control systems, electronics, advanced materials, assembly concepts, propulsion, and solar power satellite systems