Browsing Isolated Population Data

BACKGROUND: In our studies of genetically isolated populations in a remote mountain area in the center of Sardinia (Italy), we found that 80–85% of the inhabitants of each village belong to a single huge pedigree with families strictly connected to each other through hundreds of loops. Moreover, intermarriages between villages join pedigrees of different villages through links that make family trees even more complicated. Unfortunately, none of the commonly used pedigree drawing tools are able to draw the complete pedigree, whereas it is commonly accepted that the visual representation of families is very important as it helps researchers in identifying clusters of inherited traits and genotypes. We had a representation issue that compels researchers to work with subsets extracted from the overall genealogy, causing a serious loss of information on familiar relationships. To visually explore such complex pedigrees, we developed PedNavigator, a browser for genealogical databases properly suited for genetic studies. RESULTS: The PedNavigator is useful for genealogical research due to its capacity to represent family relations between persons and to make a visual verification of the links during family history reconstruction. As for genetic studies, it is helpful to follow propagation of a specific set of genetic markers (haplotype), or to select people for linkage analysis, showing relations between various branch of a family tree of affected subjects. AVAILABILITY: PedNavigator is an application integrated into a Framework designed to handle data for human genetic studies based on the Oracle platform. To allow the use of PedNavigator also to people not owning the same required informatics infrastructure or systems, we developed PedNavigator Lite with mainly the same features of the integrated one, based on MySQL database server. This version is free for academic users, and it is available for download from our sit

Immersive Interaction with Pedigree Graph Visualisations

Immersive reality technologies such as Virtual Reality(VR) and Augmented Reality (AR) offer interesting opportunities in the visualisation of data. However, despite their recent popularity, research in regards to graph data visualisation using these technologies is limited, especially so for hierarchical graphs. Pedigrees, a specific type of hierarchical data that model the breeding of plant varieties within a species are a novel and interesting application area of these visualisations. We present a study on the appropriate way to visualise pedigree graph data within immersive reality. We develop a novel immersive reality application that incorporates innovative interaction techniques, and model six different graph layouts based on a hierarchical application of existing graph layout techniques. These graph layouts include the planar, cylinder, floor, sphere, cone (force directed) and vase. These six layouts were compared with a within-subjects user study measuring task accuracy, task completion time, and qualitative sentiment from the user. The results of the study did not indicate any statistically significant superior graph layout in regards to task accuracy and task completion time. However, we found that the floor layout performed the poorest in all metrics measured. These findings indicate that most 3D layouts achieve very similar performance with not only each other but the 2D baseline planar view in regards to task accuracy or task completion time. However, the sphere layout was the most positively received by the users, even more so than the 2D baseline planar layout based on the qualitative questionnaire. As such, the advantage that is possessed by some 3D layouts (but not all) such as the sphere is how they are more positively perceived

A Formalization of Linkage Analysis

In this report a formalization of genetic linkage analysis is introduced. Linkage analysis is a computationally hard biomathematical method, which purpose is to locate genes on the human genome. It is rooted in the new area of bioinformatics and no formalization of the method has previously been established. Initially, the biological model is presented. On the basis of this biological model we establish a formalization that enables reasoning about algorithms used in linkage analysis. The formalization applies both for single and multi point linkage analysis. We illustrate the usage of the formalization in correctness proofs of central algorithms and optimisations for linkage analysis. A further use of the formalization is to reason about alternative methods for linkage analysis. We discuss the use of MTBDDs and PDGs in linkage analysis, since they have proven efficient for other computationally hard problems involving large state spaces. We conclude that none of the techniques discussed are directly applicable to linkage analysis, however further research is needed in order to investigated whether a modified version of one or more of these are applicable

A Formalization of Linkage Analysis

In this report a formalization of genetic linkage analysis is introduced. Linkage analysis is a computationally hard biomathematical method, which purpose is to locate genes on the human genome. It is rooted in the new area of bioinformatics and no formalization of the method has previously been established. Initially, the biological model is presented. On the basis of this biological model we establish a formalization that enables reasoning about algorithms used in linkage analysis. The formalization applies both for single and multi point linkage analysis. We illustrate the usage of the formalization in correctness proofs of central algorithms and optimisations for linkage analysis. A further use of the formalization is to reason about alternative methods for linkage analysis. We discuss the use of MTBDDs and PDGs in linkage analysis, since they have proven efficient for other computationally hard problems involving large state spaces. We conclude that none of the techniques discussed are directly applicable to linkage analysis, however further research is needed in order to investigated whether a modified version of one or more of these are applicable

Genealogy Reconstruction: Methods and applications in cancer and wild populations

Genealogy reconstruction is widely used in biology when relationships among entities are studied. Phylogenies, or evolutionary trees, show the differences between species. They are of profound importance because they help to obtain better understandings of evolutionary processes. Pedigrees, or family trees, on the other hand visualize the relatedness between individuals in a population. The reconstruction of pedigrees and the inference of parentage in general is now a cornerstone in molecular ecology. Applications include the direct infer- ence of gene flow, estimation of the effective population size and parameters describing the population’s mating behaviour such as rates of inbreeding. In the first part of this thesis, we construct genealogies of various types of cancer. Histopatho- logical classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. We introduce a novel algorithm to rank tumor subtypes according to the dis- similarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia and liposarcoma subtypes and then apply it to a broader group of sarcomas and of breast cancer subtypes. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. In contrast to asexually reproducing cancer cell populations, pedigrees of sexually reproduc- ing populations cannot be represented by phylogenetic trees. Pedigrees are directed acyclic graphs (DAGs) and therefore resemble more phylogenetic networks where reticulate events are indicated by vertices with two incoming arcs. We present a software package for pedigree reconstruction in natural populations using co-dominant genomic markers such as microsatel- lites and single nucleotide polymorphism (SNPs) in the second part of the thesis. If available, the algorithm makes use of prior information such as known relationships (sub-pedigrees) or the age and sex of individuals. Statistical confidence is estimated by Markov chain Monte Carlo (MCMC) sampling. The accuracy of the algorithm is demonstrated for simulated data as well as an empirical data set with known pedigree. The parentage inference is robust even in the presence of genotyping errors. We further demonstrate the accuracy of the algorithm on simulated clonal populations. We show that the joint estimation of parameters of inter- est such as the rate of self-fertilization or clonality is possible with high accuracy even with marker panels of moderate power. Classical methods can only assign a very limited number of statistically significant parentages in this case and would therefore fail. The method is implemented in a fast and easy to use open source software that scales to large datasets with many thousand individuals.:Abstract v Acknowledgments vii 1 Introduction 1 2 Cancer Phylogenies 7 2.1 Introduction..................................... 7 2.2 Background..................................... 9 2.2.1 PhylogeneticTrees............................. 9 2.2.2 Microarrays................................. 10 2.3 Methods....................................... 11 2.3.1 Datasetcompilation ............................ 11 2.3.2 Statistical Methods and Analysis..................... 13 2.3.3 Comparison of our methodology to other methods . . . . . . . . . . . 15 2.4 Results........................................ 16 2.4.1 Phylogenetic tree reconstruction method. . . . . . . . . . . . . . . . . 16 2.4.2 Comparison of tree reconstruction methods to other algorithms . . . . 28 2.4.3 Systematic analysis of methods and parameters . . . . . . . . . . . . . 30 2.5 Discussion...................................... 32 3 Wild Pedigrees 35 3.1 Introduction..................................... 35 3.2 The molecular ecologist’s tools of the trade ................... 36 3.2.1 3.2.2 3.2.3 3.2.1 Sibship inference and parental reconstruction . . . . . . . . . . . . . . 37 3.2.2 Parentage and paternity inference .................... 39 3.2.3 Multigenerational pedigree reconstruction . . . . . . . . . . . . . . . . 40 3.3 Background..................................... 40 3.3.1 Pedigrees .................................. 40 3.3.2 Genotypes.................................. 41 3.3.3 Mendelian segregation probability .................... 41 3.3.4 LOD Scores................................. 43 3.3.5 Genotyping Errors ............................. 43 3.3.6 IBD coefficients............................... 45 3.3.7 Bayesian MCMC.............................. 46 3.4 Methods....................................... 47 3.4.1 Likelihood Model.............................. 47 3.4.2 Efficient Likelihood Calculation...................... 49 3.4.3 Maximum Likelihood Pedigree ...................... 51 3.4.4 Full siblings................................. 52 3.4.5 Algorithm.................................. 53 3.4.6 Missing Values ............................... 56 3.4.7 Allelefrequencies.............................. 58 3.4.8 Rates of Self-fertilization.......................... 60 3.4.9 Rates of Clonality ............................. 60 3.5 Results........................................ 61 3.5.1 Real Microsatellite Data.......................... 61 3.5.2 Simulated Human Population....................... 62 3.5.3 SimulatedClonalPlantPopulation.................... 64 3.6 Discussion...................................... 71 4 Conclusions 77 A FRANz 79 A.1 Availability ..................................... 79 A.2 Input files...................................... 79 A.2.1 Maininputfile ............................... 79 A.2.2 Knownrelationships ............................ 80 A.2.3 Allele frequencies.............................. 81 A.2.4 Sampling locations............................. 82 A.3 Output files..................................... 83 A.4 Web 2.0 Interface.................................. 86 List of Figures 87 List of Tables 88 List Abbreviations 90 Bibliography 92 Curriculum Vitae

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented