Genetic studies on Finnish lupus erythematosus patients with cutaneous manifestations

Lupus erythematosus (LE) is a chronic, heterogeneous autoimmune disorder with abnormal immune responses, including production of autoantibodies and immune complexes. Clinical presentations of the disease range from mild cutaneous manifestations to a more generalised systemic involvement of internal organs. Cutaneous (CLE) forms are further subclassified into discoid LE (DLE), subacute cutaneous LE (SCLE) and acute cutaneous lupus erythematosus (ACLE), and may later progress to systemic disease (SLE). Both genetic and environmental factors contribute to the disease, although the precise aetiology is still elusive. Furthermore, complex gene-gene or gene-environment interactions may result in different subphenotypes of lupus. The genetic background of CLE is poorly known and only a few genes are confirmed, while the number of robust genetic associations in SLE exceeds 30. The aim of this thesis was to characterise the recruited patients clinically, and identify genetic variants conferring susceptibility to cutaneous variants of LE. Given that cutaneous and systemic disease may share underlying genetic factors, putative CLE candidate genes for genotyping were selected among those showing strong evidence of association in SLE. The correlation between relevant clinical manifestations and risk genotypes was investigated in order to find specific subphenotype associations. In addition, epistatic interactions in SLE were studied. Finally, the role of tissue degrading matrix metalloproteinases (MMP) in LE tissue injury was explored. These studies were conducted in Finnish case-control and family cohort, and Swedish case-control cohort. The clinical picture of the patients in terms of cutaneous, haematological and immunological findings resembled that described in the contemporary literature. However, the proportion of daily smokers was very high supporting the role of smoking in disease aetiology. The results confirmed that, even though clinically distinct entities, CLE and SLE share predisposing genetic factors. For the first time it was shown that known SLE susceptibility genes IRF5 and TYK2 also increase the risk of CLE. A tendency toward gene-gene interaction between these genes was found in SLE. As a remarkable novel finding, it was observed that ITGAM polymorphisms associated even more strongly to DLE than SLE, and the risk estimates were substantially higher than those reported for SLE. Several other recently identified SLE susceptibility genes showed signs of good or modest association especially in DLE. Subphenotype analyses indicated possible associations to clinical features, but marginally significant results reflected lack of sufficient power for these studies. Thorough immunohistochemical analyses of several MMPs demonstrated a role in epidermal changes and dermal tissue remodelling in diseased skin, and suggested that targeted action using selective MMP inhibitors may reduce lupus-induced damage in inflamed tissues. In conclusion, the results provide an insight into the genetics of CLE and demonstrate that genetic predisposition is at least in part shared between cutaneous and systemic variants of LE. This doctoral study has contributed IRF5, TYK2, ITGAM and several other novel genes to the so far short list of genes implicated in CLE susceptibility. Detailed examination of the function of these genes in CLE pathogenesis warrants further studies. Furthermore, the results support the need of subphenotype analysis with sample sizes large enough to reveal possible specific disease associations in order to better understand the heterogeneous nature and clinical specificities of the disease. Comprehensive analysis of clinical data suggests that smoking is an environmental triggering factor.

Essays on Risk Modeling. Applications to Portfolio and Risk Management (summary section only)

In this thesis we deal with the concept of risk. The objective is to bring together and conclude on some normative information regarding quantitative portfolio management and risk assessment. The first essay concentrates on return dependency. We propose an algorithm for classifying markets into rising and falling. Given the algorithm, we derive a statistic: the Trend Switch Probability, for detection of long-term return dependency in the first moment. The empirical results suggest that the Trend Switch Probability is robust over various volatility specifications. The serial dependency in bear and bull markets behaves however differently. It is strongly positive in rising market whereas in bear markets it is closer to a random walk. Realized volatility, a technique for estimating volatility from high frequency data, is investigated in essays two and three. In the second essay we find, when measuring realized variance on a set of German stocks, that the second moment dependency structure is highly unstable and changes randomly. Results also suggest that volatility is non-stationary from time to time. In the third essay we examine the impact from market microstructure on the error between estimated realized volatility and the volatility of the underlying process. With simulation-based techniques we show that autocorrelation in returns leads to biased variance estimates and that lower sampling frequency and non-constant volatility increases the error variation between the estimated variance and the variance of the underlying process. From these essays we can conclude that volatility is not easily estimated, even from high frequency data. It is neither very well behaved in terms of stability nor dependency over time. Based on these observations, we would recommend the use of simple, transparent methods that are likely to be more robust over differing volatility regimes than models with a complex parameter universe. In analyzing long-term return dependency in the first moment we find that the Trend Switch Probability is a robust estimator. This is an interesting area for further research, with important implications for active asset allocation.

Acidification of lakes in Finland : regional estimates of lake chemistry and critical loads

Yhteenveto: Järvien happamoituminen Suomessa: Alueellinen vedenlaatu ja kriittinen kuormitus

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Influence of sampling frequency on the estimates of runoff water quality

Tiivistelmä: Havaintotiheyden vaikutus valumavesien laatuarvioihin