FROM GENES TO GENOMES: LOCAL ADAPTATION AND ADAPTIVE POTENTIAL IN TWO ARCTIC SEABIRDS

By investigating the mechanisms underlying the evolution and the maintenance of local adaptations we can help predict how species will adapt to future environmental change. In this thesis I investigate local adaptation and adaptive potential in thick-billed and common murres (Uria lomvia and U. aalge), two arctic seabirds of international conservation concern. Thanks to the recent development of new genomic methods, I address three major themes that are relevant for both the development of evolutionary theory and conservation: 1) the role of gene flow in the origin and maintenance of adaptation; 2) levels and distribution of standing genetic variation, and their contribution to adaptive potential; and 3) the genomic mechanisms maintaining an adaptive dimorphism within a single interbreeding population. First, I review the literature on genomics of local adaptation with gene flow and find that adaptation can be maintained despite gene flow, that gene flow itself can promote adaptation, and that genetic architecture is important in the origin and maintenance of local adaptations. Second, I genotype genome-wide markers and toll-like receptor genes (TLRs) to investigate local adaptation and adaptive potential in thick-billed murres. Thick-billed murres do not show signatures of local adaptation to their breeding grounds, but outlier loci group birds according to their non-breeding distributions, suggesting that selection and/or demographic connectivity in the winter may explain patterns of differentiation in this species. Genetic variation at TLRs does not decrease with increasing latitude as predicted, but tests of selection and measures of genetic diversity suggest differences in local selective regimes at most genes. Thick-billed murres show high levels of standing genetic variation and their adaptive potential will mostly depend on rate and magnitude of environmental change. Finally, I improve and annotate the assembly of the highly heterozygous genome of the thick-billed murre. Using this assembly as a reference, I perform whole genome analyses to investigate the genomic basis of an adaptive dimorphism in Atlantic common murres. I show for the first time that a 60 kb complex copy number variant in a non-coding region maintains differences in plumage and cold adaptation despite high gene flow.

Computational Methods for Identifying Hypertrophic Cardiomyopathy using 12-lead ECG

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is - potentially fatally - obstructed. It is one of the leading causes of sudden cardiac death in young people. Electrocardiography (ECG) and Echocardiography (Echo) are the standard tests for identifying HCM and other cardiac abnormalities. The American Heart Association has recommended using a pre-participation questionnaire for young athletes instead of ECG or Echo tests due to considerations of cost and time involved in interpreting the results of these tests by an expert cardiologist. Initially we set out to develop a classifier for automated prediction of young athletes’ heart conditions based on the answers to the questionnaire. Classification results and further in-depth analysis using computational and statistical methods indicated significant shortcomings of the questionnaire in predicting cardiac abnormalities. Automated methods for analyzing ECG signals can help reduce cost and save time in the pre-participation screening process by detecting HCM and other cardiac abnormalities. Therefore, the main goal of this dissertation work is to identify HCM through computational analysis of 12-lead ECG. ECG signals recorded on one or two leads have been analyzed in the past for classifying individual heartbeats into different types of arrhythmia as annotated primarily in the MIT-BIH database. In contrast, we classify complete sequences of 12-lead ECGs to assign patients into two groups: HCM vs. non-HCM. The challenges and issues we address include missing ECG waves in one or more leads and the dimensionality of a large feature-set. We address these by proposing imputation and feature-selection methods. We develop heartbeat-classifiers by employing Random Forests and Support Vector Machines, and propose a method to classify full 12-lead ECGs based on the proportion of heartbeats classified as HCM. The results from our experiments show that the classifiers developed using our methods perform well in identifying HCM. Thus the two contributions of this thesis are the utilization of computational and statistical methods for discovering shortcomings in a current screening procedure and the development of methods to identify HCM through computational analysis of 12-lead ECG signals.

New Rigorous Decomposition Methods for Mixed-integer Linear and Nonlinear Programming

Process systems design, operation and synthesis problems under uncertainty can readily be formulated as two-stage stochastic mixed-integer linear and nonlinear (nonconvex) programming (MILP and MINLP) problems. These problems, with a scenario based formulation, lead to large-scale MILPs/MINLPs that are well structured. The first part of the thesis proposes a new finitely convergent cross decomposition method (CD), where Benders decomposition (BD) and Dantzig-Wolfe decomposition (DWD) are combined in a unified framework to improve the solution of scenario based two-stage stochastic MILPs. This method alternates between DWD iterations and BD iterations, where DWD restricted master problems and BD primal problems yield a sequence of upper bounds, and BD relaxed master problems yield a sequence of lower bounds. A variant of CD, which includes multiple columns per iteration of DW restricted master problem and multiple cuts per iteration of BD relaxed master problem, called multicolumn-multicut CD is then developed to improve solution time. Finally, an extended cross decomposition method (ECD) for solving two-stage stochastic programs with risk constraints is proposed. In this approach, a CD approach at the first level and DWD at a second level is used to solve the original problem to optimality. ECD has a computational advantage over a bilevel decomposition strategy or solving the monolith problem using an MILP solver. The second part of the thesis develops a joint decomposition approach combining Lagrangian decomposition (LD) and generalized Benders decomposition (GBD), to efficiently solve stochastic mixed-integer nonlinear nonconvex programming problems to global optimality, without the need for explicit branch and bound search. In this approach, LD subproblems and GBD subproblems are systematically solved in a single framework. The relaxed master problem obtained from the reformulation of the original problem, is solved only when necessary. A convexification of the relaxed master problem and a domain reduction procedure are integrated into the decomposition framework to improve solution efficiency. Using case studies taken from renewable resource and fossil-fuel based application in process systems engineering, it can be seen that these novel decomposition approaches have significant benefit over classical decomposition methods and state-of-the-art MILP/MINLP global optimization solvers.