Prevention of colorectal cancer in Scotland: strategies for those at increased genetic risk

The identification of people at increased genetic risk of colorectal cancer and the provision of appropriate clinical screening represents one approach to the prevention of colorectal cancer in the Scottish population. This thesis aims to contribute to current knowledge regarding the available tools for identifying those at increased genetic risk in a population, namely genetic testing and family history assessment.Key issues relating to the use of family history in this context were addressed through the analysis of a unique data set, comprising family history information reported by a colorectal cancer case or control subject at interview and the results of record linkage of this data to the Scottish Cancer Registry. Retrospective family history case-control analysis showed that individuals with an affected first-degree relative were at an increased risk of developing colorectal cancer (ORimh 2.14, 95% CI = 1.11, 4.14). Prevalence of such a family history in control subjects was 9.4% (95% CI = 4.9, 13.9). Substantial under-reporting of family history was evident, with sensitivity of interview as a means of determining a history of colorectal cancer in a first-degree relative being approximately 0.55 for both cases and controls. These studies illustrate the potential advantages of targeting people with a family history, but also highlight some of the limitations of such an approach.The genetic epidemiology of the mismatch repair genes hMLH 1 and hMSH2 and their association with colorectal cancer was considered in a systematic literature review. Although conventional epidemiological studies are lacking, there is compelling evidence to implicate mutations in these genes in the aetiology of a sub-set of colorectal cancers, with penetrance of approximately 80% in males and 40% in females. A total of 550 different published gene variants were identified, and this high degree of heterogeneity was illustrated in a unique database. This review indicates that carriers of mismatch repair gene mutations merit particular consideration in the context of colorectal cancer prevention through targeting people at increased genetic risk.Accordingly, the challenge of identifying asymptomatic mismatch repair gene mutation carriers in Scotland was addressed through the development of a computer model of cascade genetic testing, a strategy in which a mutation is identified in one family member and systematically traced through a pedigree. The model predicts that application of cascade genetic testing to colorectal cancer cases < 55 years of age over a twenty- year period would involve testing 7142 patients and 849 relatives of known carriers, and would identify 321.2 (95%CI = 305.3, 337.1) asymptomatic mutation carriers, representing approximately 27% of the estimated 1209 carriers in Scotland. Model outcomes were highly sensitive to the prevalence and penetrance of mutations, and the participation rates of those offered testing. Overall, outcomes from this computer model suggest that cascade genetic testing is potentially a useful means of identifying asymptomatic mismatch repair gene mutation carriers in Scotland. Followup work should ensure that it is also of practical importance as a tool for planning research and health policy.Identification and screening of mismatch repair gene mutation carriers is an important approach to colorectal cancer prevention, but is only relevant to a minority of people at increased genetic risk. Hence, despite inherent limitations, family history remains a crucial tool for genetic risk assessment in a population. An integrated approach to the prevention of colorectal cancer through targeting people at increased genetic risk can potentially provide substantial health benefits to a sub-group of the population, and thus contribute to the overall prevention of colorectal cancer in Scotland

Mode-locking in advection-reaction-diffusion systems: an invariant manifold perspective

Fronts propagating in two-dimensional advection-reaction-diffusion (ARD) systems exhibit rich topological structure. When the underlying fluid flow is periodic in space and time, the reaction front can lock to the driving frequency. We explain this mode-locking phenomenon using so-called burning invariant manifolds (BIMs). In fact, the mode-locked profile is delineated by a BIM attached to a relative periodic orbit (RPO) of the front element dynamics. Changes in the type (and loss) of mode-locking can be understood in terms of local and global bifurcations of the RPOs and their BIMs. We illustrate these concepts numerically using a chain of alternating vortices in a channel geometry.Comment: 9 pages, 13 figure

Accelerating the XGBoost algorithm using GPU computing

We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3x and 6x using a Titan X compared to a 4 core i7 CPU, and 1.2x using a Titan X compared to 2x Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks

Accelerating the XGBoost algorithm using GPU computing

We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and shows high performance with a variety of datasets and settings, including sparse input matrices. Individual boosting iterations are parallelised, combining two approaches. An interleaved approach is used for shallow trees, switching to a more conventional radix sort-based approach for larger depths. We show speedups of between 3x and 6x using a Titan X compared to a 4 core i7 CPU, and 1.2x using a Titan X compared to 2x Xeon CPUs (24 cores). We show that it is possible to process the Higgs dataset (10 million instances, 28 features) entirely within GPU memory. The algorithm is made available as a plug-in within the XGBoost library and fully supports all XGBoost features including classification, regression and ranking tasks

High-throughput machine learning algorithms

The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. This thesis develops specialised techniques and algorithms for a subset of computationally difficult machine learning problems. The applications under investigation are quantile approximation in the limited-memory data streaming setting, interpretability of decision tree ensembles, efficient sampling methods in the space of permutations, and the generation of large numbers of pseudorandom permutations. These specific applications are investigated as they represent significant bottlenecks in real-world machine learning pipelines, where improvements to throughput have significant impact on the outcomes of machine learning projects in both industry and research. To address these bottlenecks, we discuss both theoretical improvements, such as improved convergence rates, and hardware/software related improvements, such as optimised algorithm design for high throughput hardware accelerators. Some contributions include: the evaluation of bin-packing methods for efficiently scheduling small batches of dependent computations to GPU hardware execution units, numerically stable reduction operators for higher-order statistical moments, and memory bandwidth optimisation for GPU shuffling. Additionally, we apply theory of the symmetric group of permutations in reproducing kernel Hilbert spaces, resulting in improved analysis of Monte Carlo methods for Shapley value estimation and new, computationally more efficient algorithms based on kernel herding and Bayesian quadrature. We also utilise reproducing kernels over permutations to develop a novel statistical test for the hypothesis that a sample of permutations is drawn from a uniform distribution. The techniques discussed lie at the intersection of machine learning, high-performance computing, and applied mathematics. Much of the above work resulted in open source software used in real applications, including the GPUTreeShap library [38], shuffling primitives for the Thrust parallel computing library [2], extensions to the Shap package [31], and extensions to the XGBoost library [6]