Bayesian spatial and temporal epidemiology of non-communicable diseases and mortality

Spatial epidemiology combines spatial statistical modelling and disease epidemiology for studying geographic variation in mortality and morbidity. The effects of putative risk factors may be examined using ecological regression models. On the other hand, age-period-cohort models can be used to study the variation of mortality and morbidity through time. Bayesian hierarchical statistical models offer a flexible framework for these studies and enable the estimation of uncertainties in the results. The models are usually estimated using computer-intensive Markov chain Monte Carlo simulations. In this dissertation the first four publications present practical epidemiological studies on geographic variation in non-communicable diseases in Finland. In the last publication we study the long-time variation in all-cause mortality in several European countries. New statistical models are developed for these studies. This work provides new epidemiological information on the geographic variation of acute myocardial infarctions (AMI), ischaemic stroke and parkinsonism in Finland. An extended model for studying shared and disease specific geographic variation is presented using data on AMI and ischaemic stroke incidence. Existing results on the inverse association of water hardness and AMI are refined. New models for interpolation of geochemical data with non-detected values are presented with case studies using real data. Finally, the Bayesian age-period-cohort model is extended with versatile interactions and better prediction ability. The model is then used to study long-term variation in mortality in Europe

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications.Peer reviewe

Gene-gene interaction detection with deep learning

The extent to which genetic interactions affect observed phenotypes is generally unknown because current interaction detection approaches only consider simple interactions between top SNPs of genes. We introduce an open-source framework for increasing the power of interaction detection by considering all SNPs within a selected set of genes and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a neural network, and the interactions are quantified by Shapley scores between hidden nodes, which are gene representations that optimally combine information from the corresponding SNPs. Additionally, we design a permutation procedure tailored for neural networks to assess the significance of interactions, which outperformed existing alternatives on simulated datasets with complex interactions, and in a cholesterol study on the UK Biobank it detected nine interactions which replicated on an independent FINRISK dataset.An open-source framework combines deep learning and permutations of gene interaction neural networks to detect complex gene-gene interactions and their significance in contributions to phenotypes.Peer reviewe

Soluble HLA-DR serum levels are associated with smoking but not with acute coronary syndrome

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Matrix metalloproteinase-8 and tissue inhibitor of matrix metalloproteinase-1 predict incident cardiovascular disease events and all-cause mortality in a population-based cohort

Background Extracellular matrix degrading proteases and their regulators play an important role in atherogenesis and subsequent plaque rupture leading to acute cardiovascular manifestations. Design and methods In this prospective cohort study, we investigated the prognostic value of circulating matrix metalloproteinase-8, tissue inhibitor of matrix metalloproteinase-1 concentrations, the ratio of matrix metalloproteinase-8/ tissue inhibitor of matrix metalloproteinase-1 and, for comparison, myeloperoxidase and C-reactive protein concentrations for incident cardiovascular disease endpoints. The population-based FINRISK97 cohort comprised 7928 persons without cardiovascular disease at baseline. The baseline survey included a clinical examination and blood sampling. During a 13-year follow-up the endpoints were ascertained through national healthcare registers. The associations of measured biomarkers with the endpoints, including cardiovascular disease event, coronary artery disease, acute myocardial infarction, stroke and all-cause death, were analysed using Cox regression models. Discrimination and reclassification models were used to evaluate the clinical implications of the biomarkers. Results Serum tissue inhibitor of matrix metalloproteinase-1 and C-reactive protein concentrations were associated significantly with increased risk for all studied endpoints. Additionally, matrix metalloproteinase-8 concentration was associated with the risk for a coronary artery disease event, myocardial infarction and death, and myeloperoxidase concentration with the risk for cardiovascular disease events, stroke and death. The only significant association for the matrix metalloproteinase-8/ tissue inhibitor of matrix metalloproteinase-1 ratio was observed with the risk for myocardial infarction. Adding tissue inhibitor of matrix metalloproteinase-1 to the established risk profile improved risk discrimination of myocardial infarction (p=0.039) and death (0.001). Both matrix metalloproteinase-8 (5.2%, p <0.001) and tissue inhibitor of matrix metalloproteinase-1 (12.9%, p <0.001) provided significant clinical net reclassification improvement for death. Conclusions Serum matrix metalloproteinase-8 and tissue inhibitor of matrix metalloproteinase-1 can be considered as biomarkers of incident cardiovascular disease events and death.Peer reviewe

Modelling spatial patterns in host-associated microbial communities

Microbial communities exhibit spatial structure at different scales, due to constant interactions with their environment and dispersal limitation. While this spatial structure is often considered in studies focusing on free-living environmental communities, it has received less attention in the context of host-associated microbial communities or microbiota. The wider adoption of methods accounting for spatial variation in these communities will help to address open questions in basic microbial ecology as well as realize the full potential of microbiome-aided medicine. Here, we first overview known factors affecting the composition of microbiota across diverse host types and at different scales, with a focus on the human gut as one of the most actively studied microbiota. We outline a number of topical open questions in the field related to spatial variation and patterns. We then review the existing methodology for the spatial modelling of microbiota. We suggest that methodology from related fields, such as systems biology and macro-organismal ecology, could be adapted to obtain more accurate models of spatial structure. We further posit that methodological developments in the spatial modelling and analysis of microbiota could in turn broadly benefit theoretical and applied ecology and contribute to the development of novel industrial and clinical applications

Obstructive sleep apnoea and the risk for coronary heart disease and type 2 diabetes : a longitudinal population-based study in Finland

Objective To evaluate if obstructive sleep apnoea (OSA) modifies the risk of coronary heart disease, type 2 diabetes (T2D) and diabetic complications in a gender-specific fashion. Design and setting A longitudinal population-based study with up to 25-year follow-up data on 36 963 individuals (>500 000 person years) from three population-based cohorts: the FINRISK study, the Health 2000 Cohort Study and the Botnia Study. Main outcome measures Incident coronary heart disease, diabetic kidney disease, T2D and all-cause mortality from the Finnish National Hospital Discharge Register and the Finnish National Causes-of-Death Register. Results After adjustments for age, sex, region, high-density lipoprotein (HDL) and total cholesterol, current cigarette smoking, body mass index, hypertension, T2D baseline and family history of stroke or myocardial infarction, OSA increased the risk for coronary heart disease (HR=1.36, p=0.0014, 95% CI 1.12 to 1.64), particularly in women (HR=2.01, 95% CI 1.31 to 3.07, p=0.0012). T2D clustered with OSA independently of obesity (HR=1.48, 95% CI 1.26 to 1.73, p=9.11x10(-7)). The risk of diabetic kidney disease increased 1.75-fold in patients with OSA (95% CI 1.13 to 2.71, p=0.013). OSA increased the risk for coronary heart disease similarly among patients with T2D and in general population (HR=1.36). All-cause mortality was increased by OSA in diabetic individuals (HR=1.35, 95% CI 1.06 to 1.71, p=0.016). Conclusion OSA is an independent risk factor for coronary heart disease, T2D and diabetic kidney disease. This effect is more pronounced even in women, who until now have received less attention in diagnosis and treatment of OSA than men.Peer reviewe

FINEMAP : efficient variable selection using summary data from genome-wide association studies

Motivation: The goal of fine-mapping in genomic regions associated with complex diseases and traits is to identify causal variants that point to molecular mechanisms behind the associations. Recent fine-mapping methods using summary data from genome-wide association studies rely on exhaustive search through all possible causal configurations, which is computationally expensive. Results: We introduce FINEMAP, a software package to efficiently explore a set of the most important causal configurations of the region via a shotgun stochastic search algorithm. We show that FINEMAP produces accurate results in a fraction of processing time of existing approaches and is therefore a promising tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing projects.Peer reviewe

Changes in the fine-scale genetic structure of Finland through the 20(th) century

Information about individual-level genetic ancestry is central to population genetics, forensics and genomic medicine. So far, studies have typically considered genetic ancestry on a broad continental level, and there is much less understanding of how more detailed genetic ancestry profiles can be generated and how accurate and reliable they are. Here, we assess these questions by developing a framework for individual-level ancestry estimation within a single European country, Finland, and we apply the framework to track changes in the fine-scale genetic structure throughout the 20(th) century. We estimate the genetic ancestry for 18,463 individuals from the National FINRISK Study with respect to up to 10 genetically and geographically motivated Finnish reference groups and illustrate the annual changes in the fine-scale genetic structure over the decades from 1920s to 1980s for 12 geographic regions of Finland. We detected major changes after a sudden, internal migration related to World War II from the region of ceded Karelia to the other parts of the country as well as the effect of urbanization starting from the 1950s. We also show that while the level of genetic heterogeneity in general increases towards the present day, its rate of change has considerable differences between the regions. To our knowledge, this is the first study that estimates annual changes in the fine-scale ancestry profiles within a relatively homogeneous European country and demonstrates how such information captures a detailed spatial and temporal history of a population. We provide an interactive website for the general public to examine our results. Author summary We have inherited our genomes from our parents, who, in turn, inherited their genomes from their parents, etc. Hence, a comparison between genomes of present day individuals reveals genetic population structure due to the varying levels of genetic relatedness among the individuals. We have utilized over 18,000 Finnish samples to characterize the fine-scale genetic population structure in Finland starting from a binary East-West division and ending up with 10 Finnish source populations. Furthermore, we have applied the resulting ancestry information to generate records of how the population structure has evolved each year between 1923 and 1987 in 12 geographical regions of Finland. For example, the war-related evacuation of Karelians from Southeast Finland to other parts of the country show up as a clear, sudden increase in the Evacuated ancestry elsewhere in Finland between 1939 and 1945. Additionally, different regions of Finland show very different levels of genetic mixing in 1900s, from little mixed regions like Ostrobothnia to highly mixed regions like Southwestern Finland. To distribute the results among general public, we provide an interactive website for browsing the municipality and region-level genetic ancestry profiles atPeer reviewe