Computational Techniques for Haplotype Inference and for Local Alignment Significance

This thesis which consists of an introduction and four peer-reviewed original publications studies the problems of haplotype inference (haplotyping) and local alignment significance. The problems studied here belong to the broad area of bioinformatics and computational biology. The presented solutions are computationally fast and accurate, which makes them practical in high-throughput sequence data analysis. Haplotype inference is a computational problem where the goal is to estimate haplotypes from a sample of genotypes as accurately as possible. This problem is important as the direct measurement of haplotypes is difficult, whereas the genotypes are easier to quantify. Haplotypes are the key-players when studying for example the genetic causes of diseases. In this thesis, three methods are presented for the haplotype inference problem referred to as HaploParser, HIT, and BACH. HaploParser is based on a combinatorial mosaic model and hierarchical parsing that together mimic recombinations and point-mutations in a biologically plausible way. In this mosaic model, the current population is assumed to be evolved from a small founder population. Thus, the haplotypes of the current population are recombinations of the (implicit) founder haplotypes with some point--mutations. HIT (Haplotype Inference Technique) uses a hidden Markov model for haplotypes and efficient algorithms are presented to learn this model from genotype data. The model structure of HIT is analogous to the mosaic model of HaploParser with founder haplotypes. Therefore, it can be seen as a probabilistic model of recombinations and point-mutations. BACH (Bayesian Context-based Haplotyping) utilizes a context tree weighting algorithm to efficiently sum over all variable-length Markov chains to evaluate the posterior probability of a haplotype configuration. Algorithms are presented that find haplotype configurations with high posterior probability. BACH is the most accurate method presented in this thesis and has comparable performance to the best available software for haplotype inference. Local alignment significance is a computational problem where one is interested in whether the local similarities in two sequences are due to the fact that the sequences are related or just by chance. Similarity of sequences is measured by their best local alignment score and from that, a p-value is computed. This p-value is the probability of picking two sequences from the null model that have as good or better best local alignment score. Local alignment significance is used routinely for example in homology searches. In this thesis, a general framework is sketched that allows one to compute a tight upper bound for the p-value of a local pairwise alignment score. Unlike the previous methods, the presented framework is not affeced by so-called edge-effects and can handle gaps (deletions and insertions) without troublesome sampling and curve fitting.

Identifying Genetic Risk Factors in Canine Autoimmune Disorders

Autoimmune diseases are more common in dogs than in humans and are already threatening the future of some highly predisposed dog breeds. Susceptibility to autoimmune diseases is controlled by environmental and genetic factors, especially the major histocompatibility complex (MHC) gene region. Dogs show a similar physiology, disease presentation and clinical response as humans, making them an excellent disease model for autoimmune diseases common to both species. The genetic background of canine autoimmune disorders is largely unknown, but recent annotation of the dog genome and subsequent development of new genomic tools offer a unique opportunity to map novel autoimmune genes in various breeds. Many autoimmune disorders show breed-specific enrichment, supporting a strong genetic background. Furthermore, the presence of hundreds of breeds as genetic isolates facilitates gene mapping in complex autoimmune disorders. Identification of novel predisposing genes establishes breeds as models and may reveal novel candidate genes for the corresponding human disorders. Genetic studies will eventually shed light on common biological functions and interactions between genes and the environment. This study aimed to identify genetic risk factors in various autoimmune disorders, including systemic lupus erythematosus (SLE)-related diseases, comprising immune-mediated rheumatic disease (IMRD) and steroid-responsive meningitis arteritis (SMRA) as well as Addison s disease (AD) in Nova Scotia Duck Tolling Retrievers (NSDTRs) and chronic superficial keratitis (CSK) in German Shepherd dogs (GSDs). We used two different approaches to identify genetic risk factors. Firstly, a candidate gene approach was applied to test the potential association of MHC class II, also known as a dog leukocyte antigen (DLA) in canine species. Secondly, a genome-wide association study (GWAS) was performed to identify novel risk loci for SLE-related disease and AD in NSDTRs. We identified DLA risk haplotypes for an IMRD subphenotype of SLE-related disease, AD and CSK, but not in SMRA, and show that the MHC class II gene region is a major genetic risk factor in canine autoimmune diseases. An elevated risk was found for IMRD in dogs that carried the DLA-DRB1*00601/DQA1*005011/DQB1*02001 haplotype (OR = 2.0, 99% CI = 1.03-3.95, p = 0.01) and for ANA-positive IMRD dogs (OR = 2.3, 99% CI = 1.07-5.04, p-value 0.007). We also found that DLA-DRB1*01502/DQA*00601/DQB1*02301 haplotype was significantly associated with AD in NSDTRs (OR = 2.1, CI = 1.0-4.4, P = 0.044) and the DLA-DRB1*01501/DQA1*00601/DQB1*00301 haplotype with the CSK in GSDs (OR=2.67, CI=1.17-6.44, p= 0.02). In addition, we found that homozygosity for the risk haplotype increases the risk for each disease phenotype and that an overall homozygosity for the DLA region predisposes to CSK and AD. Our results have enabled the development of genetic tests to improve breeding practices by avoiding the production of puppies homozygous for risk haplotypes. We also performed the first successful GWAS for a complex disease in dogs. With less than 100 cases and 100 controls, we identified five risk loci for SLE-related disease and AD and found strong candidate genes involved in a novel T-cell activation pathway. We show that an inbred dog population has fewer risk factors, but each of them has a stronger genetic risk. Ongoing studies aim to identify the causative mutations and bring new knowledge to help diagnostics, treatment and understanding of the aetiology of SLE-related diseases.

Studies on Causes of Multiple Sclerosis : From Genes to Transcriptome

Multiple sclerosis (MS) is the most common cause of neurological disability in young adults, affecting more than two million people worldwide. It manifests as a chronic inflammation in the central nervous system (CNS) and causes demyelination and neurodegeneration. Depending on the location of the demyelinated plaques and axonal loss, a variety of symptoms can be observed including deficits in vision, coordination, balance and movement. With a typical age of onset at 20-40 years, the social and economic impacts of MS on lives of the patients and their families are considerable. Unfortunately the current treatments are relatively inefficient and the development of more effective treatments has been impeded by our limited understanding of the causes and pathogenesis of MS. Risk of MS is higher in biological relatives of MS patients than in the general population. Twin and adoption studies have shown that familial clustering of MS is explained by shared genetic factors rather than by shared familial environment. While the involvement of the human leukocyte antigen (HLA) genes was first discovered four decades ago, additional genetic risk factors have only recently been identified through genome-wide association studies (GWAS). Current evidence suggests that MS is a highly polygenic disease with perhaps hundreds of common variants with relatively modest effects contributing to susceptibility. Despite extensive research, the majority of these risk factors still remain to be identified. In this thesis the aim was to identify novel genes and pathways involved in MS. Using genome-wide microarray technology, gene expression levels in peripheral blood mononuclear cells (PBMC) from 12 MS patients and 15 controls were profiled and more than 600 genes with altered expression in MS were identified. Three of five selected findings, DEFA1A3, LILRA4 and TNFRSF25, were successfully replicated in an independent sample. Increased expression of DEFA1A3 in MS is a particularly interesting observation, because its elevated levels have previously been reported also in several other autoimmune diseases. A systematic review of seven microarray studies was then performed leading to identification of 229 genes, in which either decreased or increased expression in MS had been reported in at least two studies. In general there was relatively little overlap across the experiments: 11 of the 229 genes had been reported in three studies and only HSPA1A in four studies. Nevertheless, these 229 genes were associated with several immunological pathways including interleukin pathways related to type 2 and type 17 helper T cells and regulatory T cells. However, whether these pathways are involved in causing MS or related to secondary processes activated after disease onset remains to be investigated. The 229 genes were also compared with loci identified in published MS GWASs. Single nucleotide polymorphisms (SNP) in 17 of the 229 loci had been reported to be associated with MS with P-value less than 0.0001 including variants in CXCR4 and SAPS2, which were the only loci where evidence for correlation between the associated variant and gene expression was found. The CXCR4 variant was further tested for association with MS in a large case-control sample and the previously reported suggestive association was replicated (P-value is 0.0004). Finally, common genetic variants in candidate genes, which had been selected on the basis of showing association with other autoimmune diseases (MYO9B) or showing differential expression in MS in our study (DEFA1A3, LILRA4 and TNFRSF25), were tested for association with MS, but no evidence of association was found. In conclusion, through a systematic review of genome-wide expression studies in MS we have identified several promising candidate genes and pathways for future studies. In addition, we have replicated a previously suggested association of a SNP variant upstream of CXCR4 with MS. Keywords: autoimmune disease, common variant, CXCR4, DEFA1A3, HSPA1A,gene expression, genetic association, GWAS, MS, multiple sclerosis, systematic review

Intensity, productivity and efficiency in agriculture in Finland and implications for N and P fertiliser management

The purpose of this study was to evaluate intensity, productivity and efficiency in agriculture in Finland and show implications for N and P fertiliser management. Environmental concerns relating to agricultural production have been and still are focused on arguments about policies that affect agriculture. These policies constrain production while demand for agricultural products such as food, fibre and energy continuously increase. Therefore the importance of increasing productivity is a great challenge to agriculture. Over the last decades producers have experienced several large changes in the production environment such as the policy reform when Finland joined the EU 1995. Other and market changes occurred with the further EU enlargement with neighbouring countries in 2005 and with the decoupling of supports over the 2006-2007 period. Decreasing prices a decreased number of farmers and decreased profitability in agricultural production have resulted from these changes and constraints and of technological development. It is known that the accession to the EU 1995 would herald changes in agriculture. Especially of interest was how the sudden changes in prices of commodities on especially those of cereals, decreased by 60%, would influence agricultural production. The knowledge of properties of the production function increased in importance as a consequence of price changes. A research on the economic instruments to regulate productions was carried out and combined with earlier studies in paper V. In paper I the objective was to compare two different technologies, the conventional farming and the organic farming, determine differences in productivity and technical efficiency. In addition input specific or environmental efficiencies were analysed. The heterogeneity of agricultural soils and its implications were analysed in article II. In study III the determinants of technical inefficiency were analysed. The aspects and possible effects of the instability in policies due to a partial decoupling of production factors and products were studied in paper IV. Consequently connection between technical efficiency based on the turnover and the sales return was analysed in this study. Simple economic instruments such as fertiliser taxes have a direct effect on fertiliser consumption and indirectly increase the value of organic fertilisers. However, fertiliser taxes, do not fully address the N and P management problems adequately and are therefore not suitable for nutrient management improvements in general. Productivity of organic farms is lower on average than conventional farms and the difference increases when looking at selling returns only. The organic sector needs more research and development on productivity. Livestock density in organic farming increases productivity, however, there is an upper limit to livestock densities on organic farms and therefore nutrient on organic farms are also limited. Soil factors affects phosphorous and nitrogen efficiency. Soils like sand and silt have lower input specific overall efficiency for nutrients N and P. Special attention is needed for the management on these soils. Clay soils and soils with moderate clay content have higher efficiency. Soil heterogeneity is cause for an unavoidable inefficiency in agriculture.