This thesis aims to develop various statistical methods for analysing the data derived from genome wide association studies (GWAS). The GWAS often involves genotyping individual human genetic variation, using high-throughput genome-wide single nucleotide polymorphism (SNP) arrays, in thousands of individuals and testing for association between those variants and a given disease under the assumption of common disease/common variant. Although GWAS have identified many potential genetic factors in the genome that affect the risks to complex diseases, there is still much of the genetic heritability that remains unexplained. The power of detecting new genetic risk variants can be improved by considering multiple genetic variants simultaneously with novel statistical methods. Improving the analysis of the GWAS data has received much attention from statisticians and other scientific researchers over the past decade. There are several challenges arising in analysing the GWAS data. First, determining the risk SNPs might be difficult due to non-random correlation between SNPs that can inflate type I and II errors in statistical inference. When a group of SNPs are considered together in the context of haplotypes/genotypes, the distribution of the haplotypes/genotypes is sparse, which makes it difficult to detect risk haplotypes/genotypes in terms of disease penetrance. In this work, we proposed four new methods to identify risk haplotypes/genotypes based on their frequency differences between cases and controls. To evaluate the performances of our methods, we simulated datasets under wide range of scenarios according to both retrospective and prospective designs. In the first method, we first reconstruct haplotypes by using unphased genotypes, followed by clustering and thresholding the inferred haplotypes into risk and non-risk groups with a two-component binomial-mixture model. In the method, the parameters were estimated by using the modified Expectation-Maximization algorithm, where the maximisation step was replaced the posterior sampling of the component parameters. We also elucidated the relationships between risk and non-risk haplotypes under different modes of inheritance and genotypic relative risk. In the second method, we fitted a three-component mixture model to genotype data directly, followed by an odds-ratio thresholding. In the third method, we combined the existing haplotype reconstruction software PHASE and permutation method to infer risk haplotypes. In the fourth method, we proposed a new way to score the genotypes by clustering and combined it with a logistic regression approach to infer risk haplotypes. The simulation studies showed that the first three methods outperformed the multiple testing method of (Zhu, 2010) in terms of average specificity and sensitivity (AVSS) in all scenarios considered. The logistic regression methods also outperformed the standard logistic regression method. We applied our methods to two GWAS datasets on coronary artery disease (CAD) and hypertension (HT), detecting several new risk haplotypes and recovering a number of the existing disease-associated genetic variants in the literature.

Periodic behaviours in continuous media can be described with great power and economy using conceptual machinery such as the notion of a field. However periodic effects can also be `observed' in collections of discrete objects, be they individuals sending emails, fire-flies signalling to attract mates, synapses firing in the brain or photons emerging from a cavity. The origin of periodic behaviours becomes more difficult to identify and interpret in these instances; particularly for systems whose individual components are fundamentally stochastic and memoryless. This thesis describes how periodic behaviour can emerge from intrinsic fluctuations in a fully discrete system that is completely isolated from any external coherent forcing. This thesis identifies the essential elements required to produce naturally emerging periodic behaviours in a collection of interacting `particles' which are constrained to a finite set of `states', represented by the nodes of a network. The network can be identified with a type of a spatial structure throughout which particles can move by spontaneously jumping between nodes. The particles interact by affecting the rate at which other particles jump. In such systems it is the collective ensemble of particles, rather than the individual particles themselves, that exhibit periodic behaviours. The existence or non-existence of such collective periodic behaviours is attributed to the structure of the network and the form of interaction between particles that together describe the microscopic dynamics of the system. This thesis develops a methodology for deriving the macroscopic description of the ensemble of particles from the microscopic dynamics that govern the behaviour of individual particles and uses this to find key ingredients for collective periodic behaviour. In order for periodic behaviours to emerge and persist it is necessary that the microscopic dynamics be irreversible and hence violate the principle of detailed balance. However such a condition is not sufficient and irreversibility must also manifest on the macroscopic level. Simple systems that admit collective periodic behaviours are presented, analysed and used to hypothesise on the essential elements needed for such behaviour. Important general results are then proven. It is necessary that the network have more than two nodes and directed edges such that particles jump between states at different rates in both directions. Perhaps most significantly, it is demonstrated that collective periodic behaviours are possible without invoking `action at a distance' - there need not be a field providing a mechanism for the interactions between particles.

It is of interest to determine how populations of bacteria whose genes exhibit an ON/OFF switching property (phase variation) evolve over time from an initial population. By statistical analysis of two in vitro experimental Campylobacter jejuni datasets containing 28 genes assumed to be phase variable, we find evidence of small networks of genes which exhibit dependent evolutionary behaviour. This violates the assumption that the genes in these datasets do not interact with one another in the way they mutate during the division of cells, motivating the development of a model which attempts to explain evolution of such genes with factors other than mutation alone. We show that discrete probability distributions at observation times can be estimated by utilising two stochastic models. One model provides an explanation with mutation rates in genes, resembling a Markov chain under the assumption of having a near infinite population size. The second provides an explanation with both mutation and natural selection. However, the addition of selection parameters makes this model resemble a non-linear Markov process, which makes further analysis less straight-forward. An algorithm is constructed to test whether the mutation-only model can sufficiently explain evolution of single phase variable genes, using distributions and mutation rates from data as examples. This algorithm shows that applying this model to the same phase variable genes believed to show dependent evolutionary behaviour is inadequate. We use Approximate Bayesian Computation to estimate selection parameters for the mutation with selection model, whereby inference is derived from samples drawn from an approximation of the joint posterior distribution of the model parameters. We illustrate this method on an example of three genes which show evidence of dependent evolutionary behaviour from our two datasets.

Magnetoencephalography (MEG) is a neuroimaging method for mapping brain activity based on magnetic field recordings. The inverse problem associated with MEG is severely ill-posed and is complicated by the presence of high collinearity in the forward (leadfield) matrix. This means that accurate source localisation can be challenging. The most commonly used methods for solving the MEG problem do not employ sparsity to help reduce the dimensions of the problem. In this thesis we review a number of the sparse regression methods that are widely used in statistics, as well as some more recent methods, and assess their performance in the context of MEG data. Due to the complexity of the forward model in MEG, the presence of measurement-error in the leadfield matrix can create issues in the spatial resolution of the data. Therefore we investigate the impact of measurement-error on sparse regression methods as well as how we can correct for it. We adapt the conditional score and simulation extrapolation (SIMEX) methods for use with sparse regression methods and build on an existing corrected lasso method to cover the elastic net penalty. These methods are demonstrated using a number of simulations for different types of measurement-error and are also tested with real MEG data. The measurement-error methods perform well in simulations, including high dimensional examples, where they are able to correct for attenuation bias in the true covariates. However the extent of their correction is much more restricted in the more complex MEG data where covariates are highly correlated and there is uncertainty over the distribution of the error.

Acrucial practical advantage of infectious diseases modelling as a public health tool lies in its application to evaluate various disease-control policies. However, such evaluation is of limited use, unless a sufficiently accurate epidemic model is applied. If the model provides an adequate fit, it is possible to interpret parameter estimates, compare disease epidemics and implement control procedures. Methods to assess and compare stochastic epidemic models in a Bayesian framework are not well-established, particularly in epidemic settings with missing data. In this thesis, we develop novel methods for both model adequacy and model choice for stochastic epidemic models. We work with continuous time epidemic models and assume that only case detection times of infected individuals are available, corresponding to removal times. Throughout, we illustrate our methods using both simulated outbreak data and real disease data. Data augmented Markov Chain Monte Carlo (MCMC) algorithms are employed to make inference for unobserved infection times and model parameters. Under a Bayesian framework, we first conduct a systematic investigation of three different but natural methods of model adequacy for SIR (Susceptible-Infective-Removed) epidemic models. We proceed to develop a new two-stage method for assessing the adequacy of epidemic models. In this two stage method, two predictive distributions are examined, namely the predictive distribution of the final size of the epidemic and the predictive distribution of the removal times. The idea is based onlooking explicitly at the discrepancy between the observed and predicted removal times using the posterior predictive model checking approach in which the notion of Bayesian residuals and the and the posterior predictive p−value are utilized. This approach differs, most importantly, from classical likelihood-based approaches by taking into account uncertainty in both model stochasticity and model parameters. The two-stage method explores how SIR models with different infection mechanisms, infectious periods and population structures can be assessed and distinguished given only a set of removal times. In the last part of this thesis, we consider Bayesian model choice methods for epidemic models. We derive explicit forms for Bayes factors in two different epidemic settings, given complete epidemic data. Additionally, in the setting where the available data are partially observed, we extend the existing power posterior method for estimating Bayes factors to models incorporating missing data and successfully apply our missing-data extension of the power posterior method to various epidemic settings. We further consider the performance of the deviance information criterion (DIC) method to select between epidemic models.

Radial glial cells play an important role during embryonic development in mammals. They are not only important for neural production but help to organise the architecture of the neocortex. Glial cells proliferate during the development of the brain in the embryo, before differentiating to produce neurons at a rate which increases towards the end of embryonic brain development. Glial cells communicate via Adenosine tri-phosphate (ATP) mediated calcium waves, which may have the effect of locally synchronising cell cycles, so that clusters of cells proliferate together, shedding cells in uniform sheets. Hence radial glial cells are not only responsible for the production of most neocortical neurons but also contribute to the architecture of the brain. It has been argued that human developmental disorders which are associated with cortical malfunctions such as infantile epilepsies and mental retardation may involve defects in neuronal production and/or architecture and mathematical modelling may shed some light upon these disorders. This thesis investigates, among other things, the conditions under which radial glial cells' cell cycles become `phase locked', radial glia proliferation and stochastic effects. There are various models for the cell cycle and for intracellular calcium dynamics. As part of our work, we marry two such models to form a model which incorporates the effect of calcium on the cell cycle of a single radial glial cell. Furthermore, with this achieved we consider populations of cells which communicate with each other via the secretion of ATP. Through bifurcation analysis, direct numerical simulation and the application of the theory of weakly coupled oscillators, we investigate and compare the behaviour of two models which differ from each other in the time during the cell cycle at which ATP is released. Our results from this suggest that cell cycle synchronisation is highly dependent upon the timing of ATP release. This in turn suggests that a malfunction in the timing of ATP release may be responsible for some cortical development disorders. We also show how the increase in radial glia proliferation may mostly be down to radial glial cells' ability to recruit quiescent cells onto the cell cycle. Furthermore, we consider models with an additive noise term and through the application of numerical techniques show that noise acts to advance the onset of oscillatory type solutions in both models. We build upon these results and show as a proof of concept how noise may act to enhance radial glia proliferation.

Modelling and reconstruction of genetic regulatory networks has developed in a wide field of study in the past few decades, with the application of ever sophisticated techniques. This thesis looks at how models for genetic networks have been developed from simple Boolean representations to more complicated models that take into account the inherent stochasticity of the biological system they are modelling. Statistical techniques are used to help predict the interaction between genes from microarray data in order to recover genetic regulatory networks and provide likely candidates for interactions that can be experimentally verified. The use of Granger causality is applied to statistically assess the effect of one gene upon another and modifications to this are presented, with bootstrapping used to understand the variability present within the parameters. Given the large amounts of data to be analysed from microarray experiments, clustering techniques are used to help reduce the computational burden and novel algorithms are developed to make use of such clustered data. Variability within clusters is also considered, by developing a novel approach with the use of principal component analysis. These algorithms that are developed are implemented with an observed dataset from Xenopus Laevis that has many genes but few timepoints in order to assess their effectiveness under such limited data. Predictions of likely interactions between genes are provided from the algorithms developed and their limitations discussed. Using extra information is considered, where a further dataset of gene knockout data is used to verify the predictions made for one particular gene.

This thesis examines various aspects of time series and their applications. In the rst part, we study numerical and asymptotic properties of Box-Pierce family of portmanteau tests. We compare size and power properties of time series model diagnostic tests using their asymptotic c2 distribution and bootstrap distribution (dynamic and fixed design) against various linear and non-linear alternatives. In general, our results show that dynamic bootstrapping provides a better approximation of the distribution underlying these statistics. Moreover, we find that Box-Pierce type tests are powerful against linear alternatives while the CvM due to Escanciano (2006b) test performs better against non linear alternative models. The most challenging scenario for these portmanteau tests is when the process is close to the stationary boundary and value of m, the maximum lag considered in the portmanteau test, is very small. In these situations, the c2 distribution is a poor approximation of the null asymptotic distribution. Katayama (2008) suggested a bias correction term to improve the approximation in these situations. We numerically study Katayama's bias correction in Ljung and Box (1978) test. Our results show that Katayama's correction works well and conrms the results as shown in Katayama (2008). We also provide a number of algorithms for performing the necessary calculations efciently. We notice that the bootstrap automatically does bias correction in Ljung-Box statistic. It motivates us to look at theoretical properties of the dynamic bootstrap in this context. Moreover, noticing the good performance of Katayama's correction, we suggest a bias correction term for the Monti (1994) test on the lines of Katayama's correction. We show that our suggestion improves Monti's statistic in a similar way to what Katayama's suggestion does for Ljung-Box test. We also make a novel suggestion of using the pivotal portmanteau test. Our suggestion is to use two separate values of m, one a large value for the calculation of the information matrix and a smaller choice for diagnostic purposes. This results in a pivotal statistic which automatically corrects the bias correction in Ljung-Box test. Our suggested novel algorithm efciently computes this novel portmanteau test. In the second part, we implement lasso-type shrinkage methods to linear regression and time series models. We look through simulations in various examples to study the oracle properties of these methods via the adaptive lasso due to Zou (2006). We study consistent variable selection by the lasso and adaptive lasso and consider a result in the literature which states that the lasso cannot be consistent in variable selection if a necessary condition does not hold for the model. We notice that lasso methods have nice theoretical properties but it is not very easy to achieve them in practice. The choice of tuning parameter is crucial for these methods. So far there is not any fully explicit way of choosing the appropriate value of tuning parameter, so it is hard to achieve the oracle properties in practice. In our numerical study, we compare the performance of k-fold cross-validation with the BIC method of Wang et al. (2007) for selecting the appropriate value of the tuning parameter. We show that k-fold crossvalidation is not a reliable method for choosing the value of the tuning parameter for consistent variable selection. We also look at ways to implement lasso-type methods time series models. In our numerical results we show that the oracle properties of lasso-type methods can also be achieved for time series models. We derive the necessary condition for consistent variable selection by lasso-type methods in the time series context. We also prove the oracle properties of the adaptive lasso for stationary time series.

The standard memory consolidation model assumes that new memories are temporarily stored in the hippocampus and later transferred to the neocortex, during deep sleep, for long-term storage, signifying the importance of studying functional and structural cortico-hippocampal interactions. Our work offers a thorough analysis on such interactions between neocortex and hippocampus, along with a detailed study of their intrinsic dynamics, from two complementary perspectives: statistical data analysis and computational modelling. The first part of this study reviews mathematical tools for assessing directional interactions in multivariate time series. We focus on the notion of Granger Causality and the related measure of generalised Partial Directed Coherence (gPDC) which we then apply, through a custom built numerical package, to electrophysiological data from the medial prefrontal cortex (mPFC) and hippocampus of anaesthetized rats. Our gPDC analysis reveals a clear lateral-to-medial hippocampus connectivity and suggests a reciprocal information flow between mPFC and hippocampus, altered during cortical activity. The second part deals with modelling sleep-related intrinsic rhythmic dynamics of the two areas, and examining their coupling. We first reproduce a computational model of the cortical slow oscillation, a periodic alteration between activated (UP) states and neuronal silence. We then develop a new spiking network model of hippocampal areas CA3 and CA1, reproducing many of their intrinsic dynamics and exhibiting sharp wave-ripple complexes, suggesting a novel mechanism for their generation based on CA1 interneuronal activity and recurrent inhibition. We finally couple the two models to study interactions between the slow oscillation and hippocampal activity. Our simulations propose a dependence of the correlation between UP states and hippocampal spiking on the excitation-to-inhibition ratio induced by the mossy fibre input to CA3 and by a combination of the Schaffer collateral and temporoammonic input to CA1. These inputs are shown to affect reported correlations between UP states and ripples.

Telomeres are repetitive elements of DNA which are located at the ends of chromosomes. During cell division, telomeres on daughter chromomeres shorten until the telomere length falls below a critical level. This shortening restricts the number of cell divisions. In this thesis, we use mathematical modelling to study dynamics of telomere length in a cell in order to understand normal ageing (telomere shortening),Werner’s syndrome (a disease of accelerated ageing) and the immortality of cells caused by telomerase (telomere constant length maintenance). In the mathematical models we compared four possible mechanisms for telomere shortening. The simplest model assumes that a fixed amount of telomere is lost on each replication; the second supposes that telomere loss depends on telomere length; for the third case the amount of telomeres loss per division is fixed but the probability of dividing depends on telomere length; the fourth cases has both telomere loss and the probability of division dependent on telomere length. We start by developing Monte Carlo simulations of normal ageing using these four cases. Then we generalize the Monte Carlo simulations to consider Werner’s syndrome, where the extra telomeres are lost during replication accelerate the ageing process. In order to investigate how the distribution of telomere length varies with time, we derive, from the discrete model, continuum models for the four different cases. Results from the Monte Carlo simulations and the deterministic models are shown to be in good agreement. In addition to telomere loss, we also consider increases in telomere length caused by the enzyme telomerase, by appropriately extending the earlier Monte Carlo simulations and continuum models. Results from the Monte Carlo simulations and the deterministic models are shown to be in good agreement. We also show that the concentration of telomerase in cells can control their proliferative potential.