In the last decade, genome-wide association studies have helped to advance our understanding of the genetic architecture of many important traits, including diseases. However, the statistical analysis of genotype-phenotype associations remains challenging due to multiple factors. First, many traits have polygenic architectures, which means that they are controlled by a large number of variants with small individual effects. Second, as increasingly deep phenotype data are being generated there is a need for multivariate analysis approaches to leverage multiple related phenotypes while retaining computational efficiency. Additionally, genetic analyses are confronted by strong confounding factors that can create spurious associations when not properly accounted for in the statistical model. We here derive more flexible methods that allow integrating genetic effects across variants and multiple quantitative traits. To do so, we build on the classical linear mixed model (LMM), a widely adopted framework for genetic studies. The first contribution of this thesis is mtSet, an efficient mixed-model approach that enables genome-wide association testing between sets of genetic variants and multiple traits while accounting for confounding factors. In both simulations and real-data applications we demonstrate that mtSet effectively combines the advantages of variant-set and multi-trait analyses. Next, we present a new model for gene-context interactions that builds on mtSet. The proposed interaction set test (iSet) yields increased statistical power for detecting polygenic interactions. Additionally, iSet enables the identification of genetic loci that are associated with different configurations of causal variants across contexts. After benchmarking the proposed method using simulated data, we consider two applications to real datasets, where we investigate genetic effects on gene expression across different cellular contexts and sex-specific genetic effects on lipid levels. Finally, we describe LIMIX, a software framework for the flexible implementation of different LMMs. Most of the models considered in this thesis, including mtSet and iSet, are implemented and available in LIMIX. A unique aspect of the software is an inference framework that allows a large class of genetic models to be defined and, in many cases, to be efficiently fitted by exploiting specific algebraic properties. We demonstrate the utility of this software suite in two applied collaboration projects. Taken together, this thesis demonstrates the value of flexible and integrative modelling in genetics and contributes new statistical methods for genetic analysis. These approaches generalise previous models, yet retain the computational efficiency that is needed to tackle large genetic datasets.

In many schools hours of energy and effort are dedicated to making decisions and developing policy. At the school level issues of curriculum, faculty groupings and structure, strategy for staff allocations and resourcing of faculties, often results in debate before being decided upon. So often valuable time and resources are wasted in argument, disagreement and political activity. This study has been designed to determine what actually happens in the decision process, with the subject of the study a single committee. The aim of the study is to determine the style of policy development that took place and what influences affected the decisions made. The study is in two parts. The first section develops a Conceptual Framework and research questions to categorise, summarise and organise data collected from policy development processes. The Conceptual framework was designed to permit analysis of the major components of the stages of Problem Structuring, Generation of Alternatives and Recommending Policy Actions. The second section in includes further Research Questions to determine whether the process applied to developing policy was Rational, Nonrational (Incremental/Political) or a Mixed Model type. The research method used was naturalistic and qualitative in nature and in the context of a case study. The main findings were that a Mixed Model of policy development was used by the Committee with elements of both Rational and Nonrational process evident from the research data.

We collected the data for decomposition of spruce litter to determine the limit values of mass loss and to find both chemical and climate factors that influence limit values. Our data contained 28 sequences of spruce which mainly in Sweden and a small part in other places. We choose mean annual temperature (MAT) and mean annual precipitation (MAP) as climate factors and water solubles, lignin, N, P, K, Ca, Mg and Mn as chemical factors. Then we got the estimated limit values by performing a nonlinear model with mass loss and time spots, and found out the influential factors by using another linear mixed model. At the end we knew that linear mixed model is a proper and efficient approach for determining the factors, P and MAP are the significant factors and Species is a good random effect to explain the variance within groups.

On the subject of what affect voters¡¦ vote choice, political scientists for a long time emphasize three answers: party identification, candidate orientation, and issue orientation. About issue orientation, Rational Choice Theory assumes that human are rational pursuing maximizing self- interests. When voters are making their vote decisions, they would observe the issues positions of competing parties or candidates, comparing with their own positions, and then vote the party or the candidate who can represent their own positions best. Spatial Theory, from Rational Choice Theory, takes those abstract issue positions into some issue space. Both the issue positions of voters and parties could be presented by some points in the space, and the length and direction between the points can represent the differences between issue positions. There are several different models in Spatial Theory, and different models advocate different ways about how voters use the points in issue space to form their evaluations to competing parties or candidates. In this paper, we take the viewpoints of Spatial Theory to research the issue voting behavior of Taiwanese voters. First, we try to know the spatial distribution of voters¡¦ issue positions. Then we inspect the association between voters¡¦ social back- ground elements and issue position. Finally, we test three models of Spatial Theory, proximity model, directional model, and RM mixed model, to know how Taiwanese voters use issue positions to form their party-evaluation.

As a large number of single nucleotide polymorphisms (SNPs) and microsatellite markers are available, high resolution mapping employing multiple markers or multiple allele markers is an important step to identify quantitative trait locus (QTL) of complex human disease. For many complex diseases, quantitative phenotype values contain more information than dichotomous traits do. Much research has been done on conducting high resolution mapping using information of linkage and linkage disequilibrium. The most commonly employed approaches for mapping QTL are pedigree-based linkage analysis and population-based association analysis. As one of the methods dealing with multiple alleles markers, mixed models are developed to work out family-based association study with the information of transmitted allele and nontransmitted allele from one parent to offspring. For multiple markers, variance component models are proposed to perform association study and linkage analysis simultaneously. Linkage analysis provides suggestive linkage based on a broad chromosome region and is robust to population admixtures. One the other hand, allelic association due to linkage disequilibrium (LD) usually operates over very short genetic distance, but is affected by population stratification. Combining both approaches plays a synergistic role in overcoming their limitations and in increasing the efficiency and effectiveness of gene mapping.

Change in adaptive outcomes of children who are treated for brain tumors is examined using longitudinal data. The children received different types of treatment from none to any combinations of three treatments, which are surgery, radiation and chemotherapy. In this thesis, we use mixed model to find the significant variables that predict change in outcomes of communication skill, daily living skills and socialization skill. Fractional polynomial transformation method and Gompertz method are applied to build non-linear longitudinal curves. We use PRESS as the criterion to compare these two methods. Comparison analysis shows the effect of each significant variable on adaptive behaviors over time. In most cases, model with Gompertz method is better than that with Transformation method. Significant predictors of change in adaptive outcomes include Time, Gender, Surgery, SES classes, interaction between Time and Radiation, interaction between Time and Gender, interaction between Age and Gender.

No description available.

For reliable service, a power system must remain stable and capable of withstanding a wide range of disturbances especially for the large interconnected systems. In the last decade and a half and in particular after the famous blackout in N.Y. U.S.A. 1965, considerable research effort has gone in to the stability investigation of power systems. To deal with the requirements of real power systems, various stabilizing control techniques were being developed over the last decade. Conventional control engineering approaches are unable to effectively deal with system complexity, nonlinearities, parameters variations and uncertainties. This dissertation presents a non-linear control technique which relies on prediction of the large power system behaviour. One example of a large modern power system formed by interconnecting the power systems of various states is the South-Eastern Australian power network made up of the power systems of Queensland, New South Wales, Victoria and South Australia. The Model Predictive Control (MPC) for the total power system has been shown to be successful in addressing many large scale nonlinear control problems. However, for application to the high order problems of power systems and given the fast control response required, total MPC is still expensive and is structured for centralized control. This thesis develops a MPC algorithm to control the field currents of generators incorporating them in a decentralized overall control scheme. MPC decisions are based on optimizing the control action in accordance with the predictions of an identified power system model so that the desired response is obtained. Energy Function based design provides good control for direct influence items such as SVC (Static Var Compensators), FACTS (Flexible AC Transmission System) or series compensators and can be used to define the desired flux for generator. The approach in this thesis is to use the design flux for best system control as a reference for MPC. Given even a simple model of the relation between input control signal and the resulting machine flux, the MPC can be used to find the control sequence which will start the correct tracking. The continual recalculation of short time optimal control and then using only the initial control value provides a form of feedback control for the system in the desired tracking task but in a manner which retains the nonlinearity of the model.

No description available.

Spring hunting season for wild turkey (Meleagris gallopavo) in Mississippi is designed to coincide with peak gobbling activity. The Mississippi Department of Wildlife, Fisheries and Parks (MDWFP) uses brood surveys and hunter observations to forecast gobbling activity. Hunters claimed hunting season does not coincide with regional gobbling peaks. I conducted statewide surveys to assess latitudinal and climatic influences in gobbling activity and used long-term (1996-2008) MDWFP data to evaluate use as a forecasting tool. I observed ≥ 66% of all spring gobbling with an approximate 2-week difference in peak gobbling activity between northern and southern Mississippi. Gobbling in the north was influenced by temperature, wind speed, and cloud cover; in the south, only cloud cover. Long-term data performed poorly predicting gobbling activity (R2 = 0.02 – 0.047, regionally; R2 = 0.06 – 0.09, statewide). Spring hunting season captures most gobbling, including peaks. Data sources should be used cautiously to forecast gobbling activity.