Bayesian Inference for Retrospective Population Genetics Models Using Markov Chain Monte Carlo Methods

Genetics, the science of heredity and variation in living organisms, has a central role in medicine, in breeding crops and livestock, and in studying fundamental topics of biological sciences such as evolution and cell functioning. Currently the field of genetics is under a rapid development because of the recent advances in technologies by which molecular data can be obtained from living organisms. In order that most information from such data can be extracted, the analyses need to be carried out using statistical models that are tailored to take account of the particular genetic processes. In this thesis we formulate and analyze Bayesian models for genetic marker data of contemporary individuals. The major focus is on the modeling of the unobserved recent ancestry of the sampled individuals (say, for tens of generations or so), which is carried out by using explicit probabilistic reconstructions of the pedigree structures accompanied by the gene flows at the marker loci. For such a recent history, the recombination process is the major genetic force that shapes the genomes of the individuals, and it is included in the model by assuming that the recombination fractions between the adjacent markers are known. The posterior distribution of the unobserved history of the individuals is studied conditionally on the observed marker data by using a Markov chain Monte Carlo algorithm (MCMC). The example analyses consider estimation of the population structure, relatedness structure (both at the level of whole genomes as well as at each marker separately), and haplotype configurations. For situations where the pedigree structure is partially known, an algorithm to create an initial state for the MCMC algorithm is given. Furthermore, the thesis includes an extension of the model for the recent genetic history to situations where also a quantitative phenotype has been measured from the contemporary individuals. In that case the goal is to identify positions on the genome that affect the observed phenotypic values. This task is carried out within the Bayesian framework, where the number and the relative effects of the quantitative trait loci are treated as random variables whose posterior distribution is studied conditionally on the observed genetic and phenotypic data. In addition, the thesis contains an extension of a widely-used haplotyping method, the PHASE algorithm, to settings where genetic material from several individuals has been pooled together, and the allele frequencies of each pool are determined in a single genotyping.

Transforming growth factor -beta signaling through Smad3 in allergy : Studies in the mechanisms of asthma, atopic dermatitis and allergic contact reactions

Transforming growth factor β signalling through Smad3 in allergy Allergic diseases, such as atopic dermatitis, asthma, and contact dermatitis are complex diseases influenced by both genetic and environmental factors. It is still unclear why allergy and subsequent allergic disease occur in some individuals but not in others. Transforming growth factor (TGF)-β is an important immunomodulatory and fibrogenic factor that regulates cellular processes in injured and inflamed skin. TGF-β has a significant role in the regulation of the allergen-induced immune response participating in the development of allergic and asthmatic inflammation. TGF-β is known to be an immunomodulatory factor in the progression of delayed type hypersensitivity reactions and allergic contact dermatitis. TGF-β is crucial in regulating the cellular responses involved in allergy, such as differentiation, proliferation and migration. TGF-β signals are delivered from the cytoplasm to the nucleus by TGF-β signal transducers called Smads. Smad3 is a major signal transducer in TGF-β -signalling that controls the expression of target genes in the nucleus in a cell-type specific manner. The role of TGF-β-Smad3 -signalling in the immunoregulation and pathophysiology of allergic disorders is still poorly understood. In this thesis, the role of TGF-β-Smad -signalling pathway using Smad3 -deficient knock out mice in the murine models of allergic diseases; atopic dermatitis, asthma and allergic contact reactions, was examined. Smad3-pathway regulates allergen induced skin inflammation and systemic IgE antibody production in a murine model atopic dermatitis. The defect in Smad3 -signalling decreased Th2 cytokine (IL-13 and IL-5) mRNA expression in the lung, modulated allergen induced specific IgG1 response, and affected mucus production in the lung in a murine model of asthma. TGF-β / Smad3 -signalling contributed to inflammatory hypersensitivity reactions and disease progression via modulation of chemokine and cytokine expression and inflammatory cell recruitment, cell proliferation and regulation of the specific antibody response in a murine model of contact hypersensitivity. TGF-β modulates inflammatory responses - at least partly through the Smad3 pathway - but also through other compensatory, non-Smad-dependent pathways. Understanding the effects of the TGF-β signalling pathway in the immune system and in disease models can help in elucidating the multilevel effects of TGF-β. Unravelling the mechanisms of Smad3 may open new possibilities for treating and preventing allergic responses, which may lead to severe illness and loss of work ability. In the future the Smad3 signalling pathway might be a potential target in the therapy of allergic diseases.