Computational Methods for Detecting Large-Scale Chromosome Rearrangements in SNP Data

Large-scale chromosome rearrangements such as copy number variants (CNVs) and inversions encompass a considerable proportion of the genetic variation between human individuals. In a number of cases, they have been closely linked with various inheritable diseases. Single-nucleotide polymorphisms (SNPs) are another large part of the genetic variance between individuals. They are also typically abundant and their measuring is straightforward and cheap. This thesis presents computational means of using SNPs to detect the presence of inversions and deletions, a particular variety of CNVs. Technically, the inversion-detection algorithm detects the suppressed recombination rate between inverted and non-inverted haplotype populations whereas the deletion-detection algorithm uses the EM-algorithm to estimate the haplotype frequencies of a window with and without a deletion haplotype. As a contribution to population biology, a coalescent simulator for simulating inversion polymorphisms has been developed. Coalescent simulation is a backward-in-time method of modelling population ancestry. Technically, the simulator also models multiple crossovers by using the Counting model as the chiasma interference model. Finally, this thesis includes an experimental section. The aforementioned methods were tested on synthetic data to evaluate their power and specificity. They were also applied to the HapMap Phase II and Phase III data sets, yielding a number of candidates for previously unknown inversions, deletions and also correctly detecting known such rearrangements.

Modelling and Forecasting Property Rents and Returns (summary section only)

Recently, focus of real estate investment has expanded from the building-specific level to the aggregate portfolio level. The portfolio perspective requires investment analysis for real estate which is comparable with that of other asset classes, such as stocks and bonds. Thus, despite its distinctive features, such as heterogeneity, high unit value, illiquidity and the use of valuations to measure performance, real estate should not be considered in isolation. This means that techniques which are widely used for other assets classes can also be applied to real estate. An important part of investment strategies which support decisions on multi-asset portfolios is identifying the fundamentals of movements in property rents and returns, and predicting them on the basis of these fundamentals. The main objective of this thesis is to find the key drivers and the best methods for modelling and forecasting property rents and returns in markets which have experienced structural changes. The Finnish property market, which is a small European market with structural changes and limited property data, is used as a case study. The findings in the thesis show that is it possible to use modern econometric tools for modelling and forecasting property markets. The thesis consists of an introduction part and four essays. Essays 1 and 3 model Helsinki office rents and returns, and assess the suitability of alternative techniques for forecasting these series. Simple time series techniques are able to account for structural changes in the way markets operate, and thus provide the best forecasting tool. Theory-based econometric models, in particular error correction models, which are constrained by long-run information, are better for explaining past movements in rents and returns than for predicting their future movements. Essay 2 proceeds by examining the key drivers of rent movements for several property types in a number of Finnish property markets. The essay shows that commercial rents in local markets can be modelled using national macroeconomic variables and a panel approach. Finally, Essay 4 investigates whether forecasting models can be improved by accounting for asymmetric responses of office returns to the business cycle. The essay finds that the forecast performance of time series models can be improved by introducing asymmetries, and the improvement is sufficient to justify the extra computational time and effort associated with the application of these techniques.

Nanoscale studies of materials using x-ray synchrotron radiation and mesoscopic modelling

X-ray synchrotron radiation was used to study the nanostructure of cellulose in Norway spruce stem wood and powders of cobalt nanoparticles in cellulose support. Furthermore, the growth of metallic clusters was modelled and simulated in the mesoscopic size scale. Norway spruce was characterized with x-ray microanalysis at beamline ID18F of the European Synchrotron Radiation Facility in Grenoble. The average dimensions and the orientation of cellulose crystallites was determined using x-ray microdiffraction. In addition, the nutrient element content was determined using x-ray fluorescence spectroscopy. Diffraction patterns and fluorescence spectra were simultaneously acquired. Cobalt nanoparticles in cellulose support were characterized with x-ray absorption spectroscopy at beamline X1 of the Deutsches Elektronen-Synchrotron in Hamburg, complemented by home lab experiments including x-ray diffraction, electron microscopy and measurement of magnetic properties with a vibrating sample magnetometer. Extended x-ray absorption fine structure spectroscopy (EXAFS) and x-ray diffraction were used to solve the atomic arrangement of the cobalt nanoparticles. Scanning- and transmission electron microscopy were used to image the surfaces of the cellulose fibrils, where the growth of nanoparticles takes place. The EXAFS experiment was complemented by computational coordination number calculations on ideal spherical nanocrystals. The growth process of metallic nanoclusters on cellulose matrix is assumed to be rather complicated, affected not only by the properties of the clusters themselves, but essentially depending on the cluster-fiber interfaces as well as the morphology of the fiber surfaces. The final favored average size for nanoclusters, if such exists, is most probably a consequence of these two competing tendencies towards size selection, one governed by pore sizes, the other by the cluster properties. In this thesis, a mesoscopic model for the growth of metallic nanoclusters on porous cellulose fiber (or inorganic) surfaces is developed. The first step in modelling was to evaluate the special case of how the growth proceeds on flat or wedged surfaces.